Systems and methods for monitoring a patient&#39;s neurological disease state

ABSTRACT

The present invention provides a system for providing neurological disease state information to a patient. The system comprises one or more sensors that sense at least one signal that comprise feature(s) that are indicative of a neurological disease state. A signal processing assembly is in communication with the one or more sensors and processes the at least one signal to estimate the neurological disease state. A patient interface module is in communication with the signal processing assembly and communicates with a patient an output that is indicative of the patient&#39;s estimated neurological disease state.

CROSS-REFERENCE

This application is a continuation-in-part of U.S. patent applicationSer. No. 10/858,899, filed Jun. 1, 2004, which is a continuation-in-partof U.S. patent application Ser. No. 10/008,576, entitled “OPTIMAL METHODAND APPARATUS FOR NEURAL MODULATION FOR THE TREATMENT OF NEUROLOGICALDISEASE, PARTICULARLY MOVEMENT DISORDERS,” filed Nov. 11, 2001 (now U.S.Pat. No. 6,819,956), which is a continuation-in-part of U.S. patentapplication Ser. No. 09/340,326, entitled “APPARATUS AND METHOD FORCLOSED-LOOP INTRACRANIAL STIMULATION FOR OPTIMAL CONTROL OF NEUROLOGICALDISEASE,” filed Jun. 25, 1999 (now U.S. Pat. No. 6,366,813), whichclaims the benefit of U.S. Provisional Patent Application Ser. No.60/095,413, entitled “OPTIMAL METHOD AND APPARATUS FOR NEURAL MODULATIONFOR THE TREATMENT OF NEUROLOGICAL DISEASE, PARTICULARLY MOVEMENTDISORDERS,” filed Aug. 5, 1998, the complete disclosures of which areincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to neurological disease and,more particularly, to intracranial stimulation for optimal control ofmovement disorders and other neurological disease.

2. Related Art

There are a wide variety of treatment modalities for neurologicaldisease including movement disorders such as Parkinson's disease,Huntington's disease, and Restless Leg Syndrome, as well as psychiatricdisease including depression, bipolar disorder and borderlinepersonality disorders. These treatment modalities are moderatelyefficacious; however, they suffer from several severe drawbacks. Each ofthese traditional treatment modalities and their associated limitationsare described below.

One common conventional technique for controlling neurological diseaseincludes the use of dopaminergic agonists or anticholinergic agents.Medical management using these techniques requires considerableiteration in dosing adjustments before an “optimal” balance betweenefficacy and side effect minimization is achieved. Variation, includingboth circadian and postprandial variations, causes wide fluctuation insymptomatology. This commonly results in alternation between “on” and“off” periods during which the patient possesses and loses motorfunctionality, respectively.

Another traditional approach for controlling movement disorders istissue ablation. Tissue ablation is most commonly accomplished throughstereotactic neurosurgical procedures, including pallidotomy,thalamotomy, subthalamotomy, and other lesioning procedures. Theseprocedures have been found to be moderately efficacious. However, inaddition to posing risks that are inherent to neurosurgical operations,these procedures suffer from a number of fundamental limitations. Onesuch limitation is that tissue removal or destruction is irreversible.As a result, excessive or inadvertent removal of tissue cannot beremedied.

Furthermore, undesirable side effects, including compromise of visionand motor or sensory functions, are likely to be permanent conditions.In particular, bilateral interventions place the patient at considerablerisk for developing permanent neurologic side effects, includingincontinence, aphasia, and grave psychic disorders. An additionaldrawback to this approach is that the “magnitude” of treatment isconstant. That is, it is not possible to vary treatment intensity overtime, as may be required to match circadian, postprandial, and otherfluctuations in symptomatology and consequent therapeutic needs. Thus,decrease in treatment “magnitude” is not possible while an increase intreatment “magnitude” necessitates reoperation. Some adjustment ispossible through augmentation with pharmacologic treatment; however,these additional treatments are subject to the above-noted limitationsrelated to drug therapy.

Another traditional approach for controlling movement disorders andother neurological disease includes tissue transplantation, typicallyfrom animal or human mesencephalic cells. Although tissuetransplantation in humans has been performed for many years, it remainsexperimental and is limited by ethical concerns when performed using ahuman source. Furthermore, graft survival, as well as subsequentfunctional connection with intracranial nuclei, are problematic. Theyield, or percentage of surviving cells, is relatively small and is notalways predictable, posing difficulties with respect to the control oftreatment “magnitude.”

Another traditional approach for controlling neurological disease is thecontinuous electrical stimulation of a predetermined neurologicalregion. Chronic high frequency intracranial electrical stimulation istypically used to inhibit cellular activity in an attempt tofunctionally replicate the effect of tissue ablation, such aspallidotomy and thalamotomy. Acute electrical stimulation and electricalrecording and impedance measuring of neural tissue have been used forseveral decades in the identification of brain structures for bothresearch purposes as well as for target localization duringneurosurgical operations for a variety of neurological diseases. Duringintraoperative electrical stimulation, reduction in tremor has beenachieved using frequencies typically on the order of 75 to 330 Hz. Basedon these findings, chronically implanted constant-amplitude electricalstimulators have been implanted in such sites as the thalamus,subthalamic nucleus and globus pallidus.

Chronic constant-amplitude stimulation has been shown to be moderatelyefficacious. However, it has also been found to be limited by the lackof responsiveness to change in patient system symptomato logy andneuromotor function. Following implantation, a protracted phase ofparameter adjustment, typically lasting several weeks to months, isendured by the patient while stimulation parameters are interactivelyadjusted during a series of patient appointments. Once determined, an“acceptable” treatment magnitude is maintained as a constant stimulationlevel. A drawback to this approach is that the system is not responsiveto changes in patient need for treatment. Stimulation is typicallyaugmented with pharmacological treatment to accommodate such changes,causing fluctuation of the net magnitude of treatment with the plasmalevels of the pharmacologic agent.

As noted, while the above and other convention treatment modalitiesoffer some benefit to patients with movement disorders, their efficacyis limited. For the above-noted reasons, with such treatment modalitiesit is difficult and often impossible to arrive at an optimal treatment“magnitude,” that is, an optimal dose or intensity of treatment.Furthermore, patients are subjected to periods of overtreatment andundertreatment due to variations in disease state. Such disease statevariations include, for example, circadian fluctuations, postprandial(after meal) and nutrition variations, transients accompanyingvariations in plasma concentrations of pharmacological agents, chronicprogression of disease, and others.

Moreover, a particularly significant drawback to the above and othertraditional treatment modalities is that they suffer frominconsistencies in treatment magnitude. For example, with respect todrug therapy, a decrease in responsiveness to pharmacologic agentseventually progresses to eventually preclude effective pharmacologictreatment. With respect to tissue ablation, progression of disease oftennecessitates reoperation to extend pallidotomy and thalamotomy lesiondimensions. Regarding tissue transplantation, imbalances between celltransplant formation rates and cell death rates cause unanticipatedfluctuations in treatment magnitude. For continuous electricalstimulation, changes in electrode position, electrode impedance, as wellas patient responsiveness to stimulation and augmentative pharmacologicagents, cause a change in response to a constant magnitude of therapy.

Currently, magnets commonly serve as input devices used by patients withimplantable stimulators, including deep brain stimulators, pacemakers,and spinal cord stimulators. Current systems require the patient tomanually turn the system off at night time to conserve battery power anduse such magnets to maintain system power. This presents considerabledifficulty to many patients whose tremor significantly impairs armfunction, as they are unable to hold a magnet in a stable manner overthe implanted electronics module. Consequently, many patients are unableto turn their stimulators on in the morning without assistance.

What is needed, therefore, is an apparatus and method for treatment ofpatients with neurological disease in general and movement disorders inparticular that is capable of determining and providing an optimal doseor intensity of treatment. Furthermore, the apparatus and method shouldbe responsive to unpredictable changes in symptomatology and minimizealternations between states of overtreatment and undertreatment. Thesystem should also be capable of anticipating future changes insymptomatology and neuromotor functionality, and being responsive tosuch changes when they occur.

SUMMARY OF THE INVENTION

The present invention is a neurological control system for modulatingactivity of any component or structure comprising the entirety orportion of the nervous system, or any structure interfaced thereto,generally referred to herein as a “nervous system component.” Theneurological control system generates neural modulation signalsdelivered to a nervous system component through one or more intracranial(IC) stimulating electrodes in accordance with treatment parameters.Such treatment parameters may be derived from a neural response topreviously delivered neural modulation signals sensed by one or moresensors, each configured to sense a particular characteristic indicativeof a neurological or psychiatric condition. Neural modulation signalsinclude any control signal that enhances or inhibits cell activity.Significantly the neurological control system considers neural response,in the form of the sensory feedback, as an indication of neurologicaldisease state and/or responsiveness to therapy, in the determination oftreatment parameters.

In one aspect of the invention, a neural modulation system for use intreating disease which provides stimulus intensity that may be varied isdisclosed. The stimulation may be at least one of activating,inhibitory, and a combination of activating and inhibitory and thedisease is at least one of neurologic and psychiatric. For example, theneurologic disease may include Parkinson's disease, Huntington'sdisease, Parkinsonism, rigidity, hemiballism, choreoathetosis, dystonia,akinesia, bradykinesia, hyperkinesia, other movement disorder, epilepsy,or the seizure disorder. The psychiatric disease may include, forexample, depression, bipolar disorder, other affective disorder,anxiety, phobia, schizophrenia, multiple personality disorder. Thepsychiatric disorder may also include substance abuse, attention deficithyperactivity disorder, impaired control of aggression, or impairedcontrol of sexual behavior.

In another aspect of the invention, a neurological control system isdisclosed. The neurological control system modulates the activity of atleast one nervous system component, and includes at least oneintracranial stimulating electrode, each constructed and arranged todeliver a neural modulation signal to at least one nervous systemcomponent; at least one sensor, each constructed and arranged to senseat least one parameter, including but not limited to physiologic valuesand neural signals, which is indicative of at least one of diseasestate, magnitude of symptoms, and response to therapy; and a stimulatingand recording unit constructed and arranged to generate said neuralmodulation signal based upon a neural response sensed by said at leastone sensor in response to a previously delivered neural modulationsignal.

In another aspect of the invention, an apparatus for modulating theactivity of at least one nervous system component is disclosed. Theapparatus includes means for delivering neural modulation signal to saidnervous system component; and means for sensing neural response to saidneural modulation signal. In one embodiment, the delivery meanscomprises means for generating said neural modulation signal, saidgenerating means includes signal conditioning means for conditioningsensed neural response signals, said conditioning including but notlimited to at least one of amplification; lowpass filtering, highpassfiltering, bandpass filtering, notch filtering, root-mean squarecalculation, envelope determination, and rectification; signalprocessing means for processing said conditioned sensed neural responsesignals to determine neural system states, including but not limited toa single or plurality of physiologic states and a single or plurality ofdisease states; and controller means for adjusting neural modulationsignal in response to sensed neural response to signal.

Advantageously, aspects of the neurological control system are capableof incorporating quantitative and qualitative measures of patientsymptomatology and neuromotor circuitry function in the regulation oftreatment magnitude.

Another advantage of certain aspects of the present invention is that itperforms automated determination of the optimum magnitude of treatment.By sensing and quantifying the magnitude and frequency of tremoractivity in the patient, a quantitative representation of the level or“state” of the disease is determined. The disease state is monitored astreatment parameters are automatically varied, and the local or absoluteminimum in disease state is achieved as the optimal set of stimulationparameters is converged upon. The disease state may be represented as asingle value or a vector or matrix of values; in the latter two cases, amulti variable optimization algorithm is employed with appropriateweighting factors. Automated optimization of treatment parametersexpedites achievement of satisfactory treatment of the patient, reducingthe time and number of interactions, typically in physician visits,endured by the patient. This optimization includes selection ofelectrode polarities, electrode configurations stimulating parameterwaveforms, temporal profile of stimulation magnitude, stimulation dutycycles, baseline stimulation magnitude, intermittent stimulationmagnitude and timing, and other stimulation parameters.

Another advantage of certain aspects of the present invention is itsprovision of signal processed sensory feedback signals to clinicians toaugment their manual selection of optimum treatment magnitude andpattern. Sensory feedback signals provided to the clinician via aclinician-patient interface include but are not limited to tremorestimates, electromyography (EMG) signals, EEG signals, accelerometersignals, acoustic signals, peripheral nerve signals, cranial nervesignals, cerebral or cerebellar cortical signals, signals from basalganglia, signals from other brain or spinal cord structures, and othersignals.

A further advantage of certain aspects of the present invention is thatit provides modulation of treatment magnitude to compensate forpredictable fluctuations in symptomatology and cognitive and neuromotorfunctionality. Such fluctuations include those due to, for example, thecircadian cycle, postprandial and nutritional changes in symptomatology,and variations in plasma levels of pharmacologic agents.

A further advantage of certain aspects of the present invention is thatit is responsive to patient symptomatology, as tremor typically abatesduring sleep. This overcomes the above-noted problems of patientinability to hold a magnet in a stable manner over the implantedelectronics module and the resulting problem of not being able to turntheir stimulators on in the morning without assistance.

A still further advantage of certain aspects of the present invention isthat it provides prediction of future symptomatology, cognitive andneuromotor functionality, and treatment magnitude requirements. Suchpredictions may be based on preset, learned and real-time sensedparameters as well as input from the patient, physician or other personor system.

A still further advantage of certain aspects of the present invention isthat it optimizes the efficiency of energy used in the treatment givento the patient. Stimulation intensity may be minimized to provide thelevel of treatment magnitude necessary to control disease symptoms to asatisfactory level without extending additional energy deliveringunnecessary overtreatment.

Further features and advantages of the present invention, as well as thestructure and operation of various embodiments of the present invention,are described in detail below with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described with reference to the accompanyingdrawings. In the drawings, like reference numerals indicate identical orfunctionally similar elements.

FIG. 1 is a schematic diagram of one embodiment of the present inventionimplanted bilaterally in a human patient.

FIG. 2 is an architectural block diagram of one embodiment of theneurological control system of the present invention.

FIG. 3 is a block diagram of one embodiment of an intracranial recordingelectrode (ICRE) signal processor and an intracranial stimulatingelectrode (ICSE) signal processor each of which are included within thesignal processor illustrated in FIG. 2.

FIG. 4 is a schematic diagram of a globus pallidus implanted withstimulating and recording electrodes in accordance with one embodimentof the present invention.

FIG. 5 is a block diagram of one embodiment of an EMG signal processorthat is included in one embodiment of the signal processor illustratedin FIG. 2.

FIG. 6 is a block diagram of one embodiment of an EEG signal processormodule that is included in one embodiment of the signal processorillustrated in FIG. 2.

FIG. 7 is a block diagram of one embodiment of an accelerometer signalprocessor that is incorporated into certain embodiments of the signalprocessor illustrated in FIG. 2.

FIG. 8 is a block diagram of one embodiment of an acoustic signalprocessor that is included in certain embodiments of the signalprocessor illustrated in FIG. 2.

FIG. 9 is block diagram of one embodiment of a peripheral nerveelectrode (PNE) signal processor 237 that is implemented in certainembodiments of signal processor 71.

FIG. 10 is a schematic diagram of one embodiment of the signal processorillustrated in FIG. 2.

FIG. 11 is a schematic diagram of the patient-neural modulator systemillustrated in FIG. 2 illustrated to show its controller and observercomponents.

FIG. 12 is a schematic diagram of one embodiment of the control circuitillustrated in FIG. 2.

FIG. 13 is a schematic diagram of electrical stimulation waveforms forneural modulation

FIG. 14 is a schematic diagram of one example of the recorded waveforms.

FIG. 15 is a schematic block diagram of an analog switch used to connectone or an opposing polarity pair of Zener diodes across the noninvertingand inverting inputs of an intracranial recording electrode amplifier.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic diagram of one embodiment of the intracranialstimulator of the present invention implanted bilaterally in a humanpatient. In the embodiment illustrated in FIG. 1, two neurologicalcontrol systems 999 are shown implanted bilaterally. Each system 999includes a stimulating and recording unit 26 and one or moreintracranial components described below. As described in thisillustrative embodiment, the intracranial components preferably includea stimulating electrode array 37. However, it should become apparent tothose of ordinary skill in the relevant art after reading the presentdisclosure that the stimulating electrodes may also be extracranial;that is, attached to a peripheral nerve in addition to or in place ofbeing located within the cranium. As shown in FIG. 1, stimulating andrecording unit 26 of each neurological control system 999 is preferablyimplanted contralateral to the intracranial components of the device.

As one skilled in the relevant art would find apparent from thefollowing description, the configuration illustrated in FIG. 1 is justone example of the present invention. Many other configurations arecontemplated. For example, in alternative embodiments of the presentinvention, the stimulating and recording unit 26 is implantedipsilateral or bilateral to the intracranial components. It should alsobe understood that the stimulating and recording unit 26 can receiveipsilateral, contralateral or bilateral inputs from sensors and deliveripsilateral, contralateral, or bilateral outputs to a single or aplurality of intracranial stimulating electrode arrays 37. Preferably,these inputs are direct or preamplified signals from at least one of EMGelectrode array 50, EEG electrode array 51, Accelerometer Array 52,Acoustic Transducer Array 53, Peripheral Nerve Electrode Array 54, andIntracranial Recording Electrode Array 38. The signals input from thesesensors will be referred to herein as “sensory input modalities” 247.The outputs include but are not limited to one or more stimulatingcurrent signals or stimulating voltage signals to IntracranialStimulating Electrode Array 37.

In the embodiment illustrated in FIG. 1, the two unilateral systems 26are shown to receive sensory inputs from the side contralateral as wellas the intracranial stimulating electrode arrays 37. In the illustrativeembodiment, systems 26 also receive sensory inputs from intracranialrecording electrode arrays 38. As will become apparent from thefollowing description, intracranial recording electrode arrays 38 mayprovide valuable feedback information.

It should be understood that this depiction is for simplicity only, andthat any combination of ipsilateral, contralateral or bilateralcombination of each of the multiple sensory input modalities andmultiple stimulation output channels may be employed. In addition,stimulating and recording units 26 may be a single device, twocommunicating devices, or two independent devices. Accordingly, theseand other configurations are considered to be within the scope of thepresent invention. It is anticipated that stimulating and recordingunits 26, if implemented as distinct units, would likely be implanted inseparate procedures (soon after clinical introduction) to minimize thelikelihood of drastic neurological complications.

In the exemplary embodiment illustrated in FIG. 1, the intracranialstimulating electrode array 37 includes a plurality of intracranialstimulating electrodes 1, 2, 3 and 4. Array 37 may, of course, have moreor fewer electrodes than that depicted in FIG. 1. These intracranialstimulating electrodes 1-4 may be used to provide stimulation to apredetermined nervous system component. The electrical stimulationprovided by the intracranial stimulating electrodes 1-4 may beexcitatory or inhibitory, and this may vary in a manner which ispreprogrammed, varied in real-time, computed in advance using apredictive algorithm, or determined using another technique now orlatter developed.

The intracranial recording electrode arrays 38 includes intracranialrecording electrodes 5 and 6. In accordance with one embodiment of thepresent invention, the intracranial recording electrodes 5, 6 are usedto record cortical activity as a measure of response to treatment and asa predictor of impeding treatment magnitude requirements. In theillustrative embodiment, intracranial recording electrodes 5 and 6 aredepicted in a location superficial to the intracranial stimulatingelectrodes 1-4. However, this positioning may be reversed or theintracranial stimulating electrodes 1-4 and intracranial recordingelectrodes 5 and 6 may be interspersed in alternative embodiments. Forexample, these electrodes may be placed in at least one of motor cortex,premotor cortex, supplementary motor cortex, other motor cortical areas,somatosensory cortex, other sensory cortical areas, Wernicke's area,Broca's area, other cortical region, other intracranial region, andother extracranial region.

In the illustrative embodiment, an intracranial catheter 7 is providedto mechanically support and facilitate electrical connection betweenintracranial and extracranial structures. In this embodiment,intracranial catheter 7 contains one or more wires connectingextracranial stimulating and recording circuit 26 to the intracranialelectrodes, including but not limited to, intracranial stimulatingelectrodes 1-4 and intracranial recording electrodes 5, 6. The wirescontained within intracranial catheter 7 transmit stimulating electrodeoutput signal (SEOS) to intracranial stimulating electrode array 37.Such wires additionally transmit stimulating electrode input signal(SEIS) and recording electrode input signal (REIS), from intracranialstimulating electrode array 37 and intracranial recording electrodearray 38 respectively, to stimulating and recording circuit 26.

Stimulating and recording circuit 26 is protected within a circuitenclosure 44. Circuit enclosure 44 and contained components, includingstimulating and recording circuit 26 comprise stimulating and recordingunit 43. It should be understood that more or fewer of either type ofelectrode as well as additional electrode types and locations may beincorporated or substituted without departing from the spirit of thepresent invention. Furthermore, stimulating and recording circuit 26 canbe placed extra cranially in a subclavian pocket as shown in FIG. 1, orit may be placed in other extracranial or intracranial locations.

Connecting cable 8 generally provides electrical connection betweenintracranial or intracranial locations. A set of electrical wiresprovides the means for communication between the intracranial andextracranial components; however, it should be understood that alternatesystems and techniques such as radiofrequency links, optical (includinginfrared) links with transcranial optical windows, magnetic links, andelectrical links using the body components as conductors, may be usedwithout departing from the present invention. Specifically, in theillustrative embodiment, connecting cable 8 provides electricalconnection between intracranial components 246 and stimulating andrecording circuit 26. In embodiments wherein stimulating and recordingcircuit 26 has an intracranial location, connecting cable 8 would likelybe entirely intracranial. Alternatively, connecting in embodimentswherein stimulating and recording circuit 26 is implanted under scalp 10or within or attached to calvarum 9, connecting cable 8 may be confinedentirely to subcutaneous region under the scalp 10.

A catheter anchor 29 provides mechanical connection between intracranialcatheter 7 and calvarum 9. Catheter anchor 29 is preferably deep to theoverlying scalp 10. Such a subcutaneous connecting cable 8 provideselectrical connection between intracranial electrodes 246 andstimulating and recording circuit 26. Cable 8 may also connect any othersensors, including but not limited to any of sensory input modalities247, or other stimulating electrodes, medication dispensers, oractuators with stimulating and recording circuit 26.

Sensory feedback is provided to recording and stimulating unit 26 from amultiplicity of sensors, collectively referred to as sensory inputmodalities 247. Intracranial recording electrode array 38, previouslydescribed, is intracranial in location. Additional sensors, most ofwhich are located extracranially in the preferred embodiment, comprisethe remainder of sensory input modalities 247. Sensory input modalities247 provide information to stimulating and recording unit 26. As will bedescribed in greater detail below, such information is processed bystimulating and recording unit 26 to deduce the disease state andprogression and its response to therapy.

In one embodiment of the invention, a head-mounted acoustic sensor 11 isused to monitor any number of vibratory characteristics such as highfrequency head vibration, muscle vibration, and/or speech production.Head-mounted acoustic sensor 11 is connected to stimulating andrecording circuit 26 with an acoustic sensor connecting cable 30.

A head-mounted accelerometer 12 is implemented in certain embodiments ofthe present invention to monitor head movement and position with respectto gravity. Head-mounted accelerometer 12 may be mounted to anystructure or structures that enables it to accurately sense a desiredmovement. Such structures include, for example, the skull base,calvarum, clavicle, mandible, extraocular structures, soft tissues andvertebrae. Head-mounted accelerometer 12 is connected to stimulating andrecording circuit 26 with an accelerometer connecting cable 31.

A proximal electromyography (EMG) electrode array 45 is also included incertain preferred embodiments of the invention. Proximal EMG electrodearray 45 includes a positive proximal EMG electrode 13, a referenceproximal EMG electrode 14, and a negative proximal EMG electrode 15. Asone skilled in the relevant art would find apparent, proximal EMGelectrode array 45 may include any number of type of electrodes.Proximal EMG electrode array 45 is implanted in or adjacent to muscletissue. In the embodiment illustrated in FIG. 1, proximal EMG electrodearray 45 is shown implanted within the neck of the human patient.However, it should be understood that this location is illustrative onlyand that proximal EMG electrode array 45 may be implanted in or adjacentto any muscle without departing from the spirit of the presentinvention.

A proximal acoustic sensor 27 may also be implemented in the presentinvention. Proximal acoustic sensor 27 senses muscle vibration and maybe used to augment, supplement or replace EMG recording. Also, aproximal accelerometer 28 may be used to sense movement, includingtremor and voluntary activity, and orientation with respect to gravity.Proximal connecting cable 16 provides electrical connection from theproximal EMG electrodes 14 and 15, proximal acoustic sensor 27, andproximal accelerometer 28 to stimulating and recording circuit 26. Inthe illustrative embodiment, these sensors are shown connected to acommon proximal connecting cable 16. However, in alternativeembodiments, this configuration may include the use of multipleconnecting cables or implement other types of communication mediawithout departing from the present invention. It should also beunderstood from the preceding description that the number of each typeof sensor may also be increased or decreased, some sensor types may beeliminated, and other sensor types may be included without departingfrom the spirit of the present invention.

A distal EMG electrode array 47 may also be included in certainembodiments of the present invention. In such embodiments, distal EMGelectrode array 47 typically includes a positive distal EMG electrode17, a reference distal EMG electrode 42, and a negative distal EMGelectrode 18. Positive distal EMG electrode 17 is connected tostimulating and recording circuit 26 by positive distal EMG connectingcable 20. Negative distal EMG electrode 18 is connected to stimulatingand recording circuit 26 by negative distal EMG connecting cable 21.Reference distal EMG electrode 42 is connected to stimulating andrecording circuit 26 by reference distal EMG connecting cable 48.

In other embodiments, a distal acoustic sensor 19 is connected tostimulating and recording circuit 26 by distal acoustic connecting cable22. Distal accelerometer 33 is connected to stimulating and recordingcircuit 26 by distal accelerometer connecting cable 34. Distalaccelerometer 33 is connected to stimulating and recording circuit 26 bydistal accelerometer connecting cable 34.

In the embodiment illustrated in FIG. 1, distal EMG electrode array 47,distal acoustic sensor 19, and distal accelerometer 33 are shown locatedin the shoulder region. However, the distal EMG electrode array 47 maybe located in other locations, including, for example, the masseter,temporalis, stemocleidomastoid, other portion of the head and neck,pectoralis, torso, abdomen, upper extremities, lower extremities, andother locations. The number of each type of sensor may be increased ordecreased, some sensor types may be eliminated, and other sensor typesmay be included without departing from the spirit of the presentinvention.

An enclosure-mounted EMG electrode array 46 is illustrated in FIG. 1.Enclosure-mounted EMG electrode array 46 includes enclosure-mountedpositive EMG electrode 23, enclosure-mounted negative EMG electrode 24and enclosure-mounted reference EMG electrode 25, all of which areattached to the circuit enclosure 44 that encloses stimulating andrecording unit 26. The circuit enclosure 44 is preferably included toprovide robustness against potential lead entanglement and fracture. Inone particular embodiment, circuit enclosure 44 is constructed oftitanium and epoxy, or other single or combination of bio-compatiblematerials. Enclosure-mounted acoustic sensor 35 and enclosure-mountedaccelerometer 36 are mounted to stimulating and recording unit 43. Thenumber of each type of sensor may be increased or decreased, theirlocations changed, some sensor types eliminated, and other sensor typesincluded without departing from the spirit of the present invention.

In the embodiment illustrated in FIG. 1, EEG electrodes 39, 40, 41 areprovided. The EEG electrodes may be mounted directly to connecting cable8 or may be connected via intermediate cables. Any one of the numerousstandard and new electrode configurations, or montages, may be employedin EEG electrodes 39-41 without departing from the present invention.

In one embodiment, a proximal peripheral nerve electrode array 98 isconnected to stimulating and recording circuit 26 by proximal peripheralnerve electrode array connecting cable 100. Proximal peripheral nerveelectrode array 98 is shown located in the neck region. In this locationproximal peripheral nerve electrode array 98 can interface with thevagus nerve, spinal accessory nerve, or nerve arising from cervicalroots.

A distal peripheral nerve electrode array 99 is connected to stimulatingand recording circuit 26 by distal peripheral nerve electrode arrayconnecting cable 32. Distal peripheral nerve electrode array 99 is shownlocated by the proximal arm, in position to interface with the brachialplexus or proximal arm nerve. One or more of these peripheral nerveelectrode arrays may be implanted in these or other locations, includingbut not limited to the head, cranial nerves, neck, torso, abdomen, upperextremities, and lower extremities, without departing from the presentinvention.

In one preferred embodiment, the peripheral nerve electrode arrays areeach comprised of three epineural platinum-iridium ring electrodes, eachin with an internal diameter approximately 30% larger than that of theepineurium, longitudinally spaced along the nerve. Electrodes ofdiffering dimensions and geometries and constructed from differentmaterials may alternatively be used without departing from the presentinvention. Alternative electrode configurations include but are notlimited to epineural, intrafascicular, or other intraneural electrodes;and materials include but are not limited to platinum, gold, stainlesssteel, carbon, and other element or alloy.

FIG. 2 is an architectural block diagram of one embodiment of theneurological control system 999 of the present invention for modulatingthe activity of at least one nervous system component in a patient. Asused herein, a nervous system component includes any component orstructure comprising an entirety or portion of the nervous system, orany structure interfaced thereto. In one preferred embodiment, thenervous system component that is controlled by the present inventionincludes the globus pallidus internus. In another preferred embodiment,the controlled nervous system component is the subthalamic nucleus.

The neurological control system 999 includes one or more implantablecomponents 249 including a plurality of sensors each configured to sensea particular characteristic indicative of a neurological or psychiatriccondition. One or more intracranial (IC) stimulating electrodes in an ICstimulating electrode array 37 delivers a neural modulation signal tothe same or other nervous system component as that being monitored bythe system 26. One or more sensors 38, 51, 52, 53, and 54 sense theoccurrence of neural responses to the neural modulation signals.Stimulating and recording unit 26 generates the neural modulation signalbased on the neural response sensed by the sensors.

The neurological control system 999 preferably also includes a patientinterface module 55 and a supervisory module 56. A control circuit 72(described below) is communicably coupled to the patient interfacemodule 55 and receives signal inputs from and provides signal outputs topatient interface module 55 and supervisory module 56. In one preferredembodiment, patient interface module 55 and supervisory module 56 remainexternal to the body of the patient. However either of these devices maybe connected via percutaneous leads or be partially or totally implantedwithout departing from the present invention.

Patient interface module 55 and supervisory module 56 facilitateadjustment of control parameters, monitoring of disease state,monitoring of response to therapy, monitoring of stimulating andrecording circuit 26, monitoring of impedance and other characteristicsof intracranial stimulating electrode array 37, monitoring ofphysiologic parameters, monitoring of vital signs, monitoring of anyother characteristic or function of components of the present invention,including but not limited to the stimulating and recording circuit 26,stimulating and recording unit 43, circuit enclosure 44, EMG electrodearray 50, EEG electrode array 51, accelerometer array 52, acoustictransducer array 53, peripheral nerve electrode array 54, andintracranial recording electrode array 38. Such monitoring andadjustment is accomplished through the use of any well knownbi-directional communication between control circuit 72 and supervisorymodule 56. In one preferred embodiment, a radio frequency link isemployed. In alternative embodiments, other communication technologies,including but not limited to optical, percutaneous, or electromagnetic,may be used.

In one preferred embodiment, patient interface module 55 and supervisorymodule 56 are placed adjacent to the patients garments overlying theimplanted stimulating and recording unit 43. When neurological controlsystem 999 is turned on in this position, a communications handshakingprotocol is executed. Communication handshaking routines are known tothose or ordinary skill in the art, and they enable establishment of acommunication rate and protocol and facilitate mutual identification ofdevices. Patient interface module 55 automatically downloads parametersfrom stimulating and recording circuit 26 and stores values of suchparameters in a memory. When the transfer of these parameter values iscomplete, patient interface module 55 emits a audible signal such as aseries of beeps, and the patient turns off patient interface module 55and removes it from its position overlying the implanted stimulating andrecording unit 43. Parameter values may then be retrieved by the patientby a routine including but not limited to a menu driven interface, andthe values may be transmitted via telephone conversation or othercommunication method to a health care professional. Supervisory module56 operates in the same manner with one addition; a step is providedduring which the health care professional may upload parameters tostimulating and recording circuit 26 to alter its function including bymeans of changing parameters including but not limited to control lawsgains and thresholds, filter parameters, signal processing parameters,stimulation waveform modes (including at least one of current regulated,voltage regulated, frequency regulated, or pulse width regulated), andstimulation waveform parameters.

Control laws, well known to those of ordinary skill in the field ofcontrol theory, are defined by a set of parameters specific to theparticular control law. Common parameters include preset gains,threshold levels, saturation amplitudes, sampling rates, and others.Adaptive controllers change in response to the behavior of the systembeing controlled; as such, in addition to preset parameters, adaptivecontrollers possess a set of varying parameters. These varyingparameters contain information indicative of the behavior of the systembeing controlled; downloading of these parameters provides one set ofmeasures of the disease state and its response to therapy.

Such monitoring includes observation of time history of disease state,stimulation parameters, response to therapy, and control law parameters,including time-varying adaptive controller parameters. Such adjustmentsincludes modification of actual stimulation parameters and allowableranges thereof, including but not limited to pulse width, pulseamplitude, interpulse interval, pulse frequency, number of pulses perburst frequency. Adjustments can further include modification of actualcontrol law parameters and allowable ranges thereof, including but notlimited to gains, thresholds and sampling rates of said stimulationwaveforms. Signal processor 71 contains signal processor modules foreach of the sensory input modalities 247. Signal processing algorithmsfor each of the said sensory input modalities 247 may be independent.Additionally, signal processing algorithms the said sensory inputmodalities 247 may be coupled, such that the processing of one of thesensory input modalities 247 is dependent on another of the sensoryinput modalities 247. Adjustments may additionally include modificationof actual signal processor parameters and allowable ranges thereof,including but not limited to gains, filter cutoff frequencies, filtertime constants, thresholds, and sampling rates. In a preferredembodiment, the stimulation and control law parameters are stored in atleast one of random access memory and central processing unit registers(not shown).

It is anticipated that patient interface module 55 is to be used by thepatient, a family member or associate, or home health care personnel tomonitor the functions and performance of neurological control system999. In such an embodiment, the use of the patient interface module 55is restricted to monitoring operations; adjustment of stimulation andcontrol parameters is not enabled. However, adjustment of all or asubset of stimulation and control parameters (described below) may befacilitated by patient interface module 55 without departing from thepresent invention. Supervisory module 56, on the other hand, is used bya physician or other health care personnel to monitor function andperformance of neurological control system 999 and to adjust stimulationand control parameters. Control parameters controlled by patientinterface module 55 and supervisory module 56 include allowablestimulation magnitude range, such as maximum combination of stimulationvoltage, current, pulse width, pulse frequency, train frequency, pulsetrain count, pulse train duration. Control parameters may also includevariables and constants used to define control laws implemented incontrol circuit 72. Such control parameters include, but are not limitedto, control law gains 197-203, and other parameters for control laws,including but not limited to proportional controller 230, differentialcontroller 204, integral controller 205, nonlinear controller 206,adaptive controller 207, sliding controller 208, model referencecontroller 209, and other controllers. In addition, amplitudes for othercontroller parameters, including but not limited to amplitudes forcontroller weights 210-216 may be set by supervisory module 56.Additionally, the parameters specifying the maximum amplitudes, orsaturation values, may be set by supervisory module 56. Control circuit72 (FIG. 12) will be described in detail below.

The majority of the computation accomplished by stimulating andrecording circuit 26 is performed in signal conditioning unit 76, signalprocessor 71, and control circuit 72; the algorithms and behavior ofwhich are determined by corresponding sets of control parameters, ofwhich some may be set by the supervisory module 56 and a typically morerestricted set by patient interface module 55. In one embodiment,control parameters further includes signal conditioning parameters.Signal conditioning parameters may include, for example, amplifiergains, filter gains and bandwidths, threshold values, and otherparameters. In certain embodiments, control parameters additionallyinclude signal processing parameters, including envelope determinatorgains and time constants, filter passbands, filter gains, thresholdvalues, integrator gains, analyzer parameters, disease state estimatorparameters, artifact rejecter thresholds, envelope determinator timeconstants, rectifier parameters, spectral analyzer parameters and timerparameters.

In the illustrative embodiment described herein, control parametersfurther include spike detector 188 (FIG. 9) parameters, spikecharacterizer 189 (FIG. 9) parameters, spike analyzer 190 (FIG. 9)parameters, spectral energy characterizer 192 (FIG. 9) parameters,spectral energy analyzer 193 (FIG. 9) parameters, aggregate diseasestate estimator 195 (FIG. 10) parameters.

In accordance with the present invention, tremor are quantified andmonitored by any sensors over time as indicators of disease state. Suchsensors include but are not limited to EMG electrode array 50, EEGelectrode array 51, accelerometer array 52, acoustic transducer array53, peripheral nerve electrode array 54, intracranial recordingelectrode array 38, and intracranial stimulating electrode array 37. Inone particular embodiment, the sensed tremor characteristics include,but are not limited to, magnitude, frequency, duration and frequency ofoccurrence of tremors. Changes in these and other parameters arecompared to current levels of, and changes in, treatment parameters.These changes are then used by aggregate disease state estimator 195 toestimate the response to therapy as functions of various electricalstimulation treatment parameters. Electrical stimulation treatmentparameters are adjusted by control circuit 72 in real-time to provideoptimal control of disease state.

Modulation parameters are optimized to achieve at least one ofminimization of disease state, minimization of symptoms of disease,minimization of stimulation magnitude, minimization of side effects, andany constant or time-varying weighted combination of these. Patientinterface module 55 and supervisory module 56 also preferably monitorthe function and operation of other components of neurological controlsystem 999, including stimulating and recording unit 26 and implantedcomponents 249.

Stimulating and recording unit 26 receives and processes signalsgenerated by implanted components 249 to provide conditioned signals78-84 to a signal processor 71. For each type of implanted components249 coupled to stimulating and recording unit 26, signal conditioningcircuit 76 preferably includes an associated amplifier and filter. Eachamplifier and associated filter is configured to receive and process thesignal generated by the associated one of the set of sensors 38, 51, 52,53, and 54.

In the illustrative embodiment, implanted components 249 include anelectromyography (EMG) electrode array 50 which generate EMG signals.Preferably, EMG electrode array 50 comprises of all EMG electrodesimplemented in the particular embodiment of the present invention. Theseinclude, in the exemplary embodiment illustrated in FIG. 1, proximal EMGelectrode array 45, enclosure-mounted EMG electrode array 46 and distalEMG electrode array 47. Array 50 may also include, for example, EMGelectrodes implanted in the head or other location, and surface EMGelectrodes.

Implanted components 249 also include an electroencephalography (EEG)electrode array 51 which generate EEG signals and accelerometer array 52which generates acceleration signals. EEG electrodes 39, 40, 41illustrated in FIG. 1 are representative of EEG electrode array 51. EEGelectrodes 39-41 may be mounted directly to connecting cable 8 orconnected via intermediate cables. EEG electrode array 51 may includemore or fewer elements than EEG electrodes 39-41 depicted; and any ofnumerous standard and new electrode configurations, or montages, may beemployed without departing from the present invention.

Accelerometer array 52, which produces well-known acceleration signals,preferably includes all accelerometers implemented in the patientassociated with the present invention. For example, in the embodimentillustrated in FIG. 1, accelerometer array 52 includes head-mountedaccelerometer 12, proximal accelerometer 28, enclosure-mountedaccelerometer 36 and distal accelerometer 33. Accelerometer array 52 mayinclude more or fewer accelerometers than these accelerometers, andaccelerometers of any types and locations may be employed withoutdeparting from the present invention.

Acoustic transducer array 53 includes all acoustic sensors utilized bythe present invention. In the exemplary embodiment illustrated in FIG.1, acoustic transducer array 53, includes head-mounted acoustic sensor11, proximal acoustic sensor 27, enclosure-mounted acoustic sensor 35and distal acoustic sensor 19. It should be understood that acoustictransducer array 53 may include more or fewer elements than saidacoustic sensors listed above; and any of numerous acoustic sensor typesand locations may be employed without departing from the presentinvention.

Peripheral nerve electrode array 54 generates peripheral neural signals,including but not limited to efferent and afferent axonal signals.Preferably, peripheral nerve electrode array 54 includes all peripheralnerve electrodes implemented in present invention. For example, in theillustrative embodiment illustrated in FIG. 1, peripheral nerveelectrode array 54 includes proximal peripheral nerve electrode array 98and distal peripheral nerve electrode array 99. The single or pluralityof individual peripheral nerve electrode arrays which compriseperipheral nerve electrode array 54 may be implanted in the illustratedor other locations, as noted above.

Intracranial (IC) recording electrode array 38 generates central neuralsignals, including but not limited to cortical, white matter, and deepbrain nuclear signals. Neural activity to be sensed includes but is notlimited to that found in the primary motor cortex, premotor cortex,supplementary motor cortex, somatosensory cortex, white matter tractsassociated with these cortical areas, the globus pallidus internalsegment, the globus pallidus external segment, the caudate, the putamen,and other cortical and subcortical areas. As one of ordinary skill inthe relevant art will find apparent, the present invention may includeadditional or different types of sensors that sense neural responses forthe type and particular patient. Such sensors generate sensed signalsthat may be conditioned to generate conditioned signals, as describedbelow. One example of the placement of these electrodes is describedabove with reference to the embodiment illustrated in FIG. 1. Manyothers are contemplated by the present invention.

As noted, for each of the different types of sensors included inimplanted components 249, signal conditioning circuit 76 includes anassociated amplifier and filter in the illustrative embodiment.Accordingly, signal conditioning circuit 76 includes an EMG amplifier 59and filter 66, each constructed and arranged to amplify and filter,respectively, the EMG signals received from EMG electrode array 50.Similarly, signal conditioning circuit 76 also includes an EEG amplifier60 and filter 67, accelerometer (ACC) amplifier 61 and filter 68,acoustic (ACO) amplifier 62 and filter 69, peripheral nerve electrode(PNE) amplifier 63 and filter 70 and intracranial (IC) recordingelectrode (ICRE) amplifier 58 and filter 65.

Simplifiers 57-63 may be single or multi-channel amplifiers dependingupon the number of electrodes with which it interfaces. In one preferredembodiment, amplifiers 57-63 are physically located in the sameenclosure as filters 64-70; that is, in a single signal conditioningcircuit 76. Preferably, signal conditioning circuit 76 is physicallycontained within stimulating and recording unit 102. However, amplifiers57-63 may be located separately from stimulating recording unit 102. Forexample, amplifiers 57-63 may be affixed to or situated proximate totheir associated electrode arrays 38, 50-54. This arrangementfacilitates the preamplification of the associated signals generated bythe associated electrode arrays 38, 50-54, increasing thesignal-to-noise ratio of the signals. Amplifiers 57-63 may be any knownvoltage amplifier now or later developed suitable for amplifying theparticular signals generated by their associated electrodes.

As noted, the amplified signals are passed to their associated filters64-70 as shown in FIG. 2. As with amplifiers 57-59, filters 64-70 may bephysically separate from or incorporated into signal conditioningcircuit 76 and stimulating and recording unit 26. In one preferredembodiment, filters 64-70 are low pass filters having a cut-offfrequency of, for example, 3,000 Hz. In alternative embodiments, filters64-70 may include a notch filter to remove, for example, 60 Hz noise, orother types of filters appropriate for the type of signals generated bythe associated sensors 38, 51, 52, 53, and 54. Selection of theappropriate frequencies for the cut-off and notch filter frequencies isconsidered to be well known in the relevant art and within the scope ofthe present invention. Filters 66-70, 65 and 64 generate conditionedsensed signals 84, 83 and 78-82, respectively.

Signal processor 71 processes the conditioned sensed neural responsesignals 78-84 generated by signal conditioning circuit 76 in accordancewith the present invention to determine neural system states. Signalprocessor 71 generally performs well known filtering operations in thetime and frequency domains. In one preferred embodiment, the neuralsystem states include one or more physiologic or disease states. Signalprocessor 71, which can be implemented in a fast microprocessor, a DSP(digital signal processor) chip, or as analog circuitry, for example, isdescribed in detail below.

Control circuit 72, responsive to the signal processor 71, patientinterface module 55 and supervisory module 56, adjusts the magnitude ofa neural modulation signal in response to the sensed neural response.Signal processor 71 extracts relevant information from the sensedcondition signals, and control circuit 72 uses this extractedinformation in the calculation of an output neuromodulation signal (NMS)998. Neuromodulation signal 998 subsequently travels along stimulatoroutput path 111 to IC stimulating electrode array 37. In one embodiment,control circuit 72 is a state machine, utilizing current and past systembehavior in the calculation of a control signal. In an alternativeembodiment, control circuit 72 includes an embedded microprocessor toprocess nonlinear control laws. Alternative embodiments of the controlcircuit 72 appropriate for the particular application may be also beused.

Control circuit 72 receives control law selection information, controllaw parameter information, stimulation waveform parameter rangeinformation, stimulation modulation mode, output stage regulation mode,and medication dose and timing information from patient interface module55 and supervisory module 56. The waveform parameter or parameters whichare modulated by control law output signal U 997 are determined by thestimulation modulation mode; these parameters include but are notlimited to pulse amplitude, pulse width, pulse frequency, pulses perburst, and burst frequency. Selection between regulation of pulsevoltage or pulse current as the regulated pulse amplitude is determinedby the output stage regulation mode.

Control circuit 72 provides stimulation waveform parameter historyinformation, disease state history information, control law statevariable history information, control law error history information,control law input variable history information, control law outputvariable history information, stimulating electrode impedance historyinformation, sensory input history information, battery voltage historyinformation, and power consumption history information to patientinterface module 55 and supervisory module 56.

Provision of stimulating electrode impedance history information allowsmonitoring of stimulating electrode performance and functionality. If anelectrode is determined to be fractured, shorted, or encapsulated byfibrotic tissue, any of various control law parameters, output stageparameters, and waveform range parameters may be adjusted to allowcompensation for these changes. Additionally, the Neuromodulation Signal(NMS) 998 may be delivered to different sets of electrodes to insurethat it reaches neural tissue 250. Sensory input history informationallows evaluation of validity of any given sensory input. This is usefulin determining the functionality of a given sensor and serves as anindicator for sensor replacement or adjustment of the signal processingparameters or algorithm or the control law parameters or algorithm tocontinue to generate reliable disease state estimate signals X andcontrol law outputs U despite the loss of any particular individual orset of sensory signals.

Signal processor 71 receives amplifier gain setting information, filterparameter information, weighting information, and disease stateestimator parameter and algorithm information from patient interfacemodule 55 and supervisory module 56. The function and operation ofpatient interface module 55 and supervisory module 56 are describedabove. As noted, patient interface module 55 may be used by the patientor home health care personnel to monitor disease state, stimulationparameters, and response to therapy. Limited adjustment of stimulationparameters and ranges is facilitated. Patient interface module 55 may beused by the patient or home health care personnel to provide informationto the physician, avoiding the need for an office visit for theobtainment of said information.

Patient information module 55 queries signal processor 71 for presentand time histories of monitored values. Time histories of selectedvariables in signal processor 71 and control circuit 72 are stored inmemory module 240 for subsequent retrieval by patient interface module55 and supervisory module 56. Selected variables include but are notlimited to disease state, tremor frequency, tremor magnitude, EMGmagnitude, EMG frequency spectra (EMG magnitude within frequencyranges), and acceleration of limb, head, mandible, or torso. Selectedvariables may also include disease state, frequency spectra of limb,torso, and head movements, as determined by EMG and accelerometersignals.

Stimulating and recording unit 26 also includes an output stage circuit77. Output stage circuit 77 takes for an input the control law outputsignal U, which may be comprised of a single or multiplicity of channelsor signals, from control circuit 72. This control law output signal U997 modulates the magnitude of the sequence of waveforms comprising thedesired output neuromodulation signal (NMS_(D)) which is produced byoutput stage circuit 77 and delivered via intracranial stimulatingelectrode array 37 to neural tissue 250.

Output stage circuit 77 generates a neuromodulation signal (NMS_(D)) 998with a magnitude specified by control law output signal U 997 receivedfrom control circuit 72. In one preferred embodiment, the waveformparameter of the desired output neuromodulation signal (NMS_(D)) whichis modulated by control law output signal U is the stimulation currentmagnitude. The capability to specifically modulate the stimulationcurrent confers efficacy resistance to perturbations or changes inelectrode impedance. Presently implanted systems suffer from a declinein efficacy which results from an increase in electrode impedance whichaccompanies the normal tissue response to a foreign body, that isfibrotic encapsulation of the electrode. In this design taught in thepresent invention, a the magnitude of the current delivered to theneural tissue 250 will not vary as the electrode becomes encapsulatedwith fibrotic tissue or its impedance otherwise changes over time. Afurther advantage conferred by current modulation is the ability tomonitor electrode impedance. If a current-modulated waveform, preferablya sinusoid, is delivered to the electrodes, and the resultant voltagepotential waveform is concurrently monitored, the relative magnitudesand phase shifts of these waveforms may be computed. From thesemagnitudes and phases, the complex impedance and hence the resistive andcapacitive components of the electrode impedance may be calculated.

In an alternative embodiment, the waveform parameter of the desiredoutput neuromodulation signal (NMS_(D)) which is modulated by controllaw output signal U 997 is the stimulation voltage magnitude. Thisdesign would not enjoy the independence of the stimulation current andefficacy from impedance variation enjoyed by the embodiment describedabove. If fibrosis was uneven around the surface of the electrode, thisembodiment would avoid potentially undesirably large current densitiesalong narrow tracts of remaining low resistance unfibrosed regions ofneural tissue 250.

Alternatively, regulation of stimulus pulse width may be desired. Incertain circuit implementations, the available resolution or bits forspecifying the magnitude of pulse width may be greater than that forspecifying the pulse voltage or current. In such a case, if finercontrol of the magnitude of Neuromodulation signal (NMS) 998 is desiredthan is provided by the control of pulse current or pulse voltage, thenit may be desirable to modulate the pulse width. Furthermore, thespatial neuron recruitment characteristics of a pulse width modulatedneuromodulation signal (NMS) 998 may provide a more linear, predictable,or controllable response than that obtained with current or voltagemodulation. Selection between regulation of pulse voltage, pulsecurrent, or pulse width as the regulated pulse amplitude parameter isdetermined by the output stage regulation mode, which may be set usingsupervisory module 56. In alternative embodiments, the modulation ofpulse frequency and the modulation of the number of pulses per burst areregulated. As one of ordinary skill in the relevant art would findapparent. Other such characteristics may be regulated in addition to orinstead of the ones noted above.

Output stage circuit 77 includes a pulse generator 73, an outputamplifier 74 and a multiplexor 75. Pulse generator 73 generates one ormore stimulus waveforms, each of which is characterized by severalparameters, including but not limited to pulse amplitude, pulse width,pulse frequency, number of pulses per burst, and burst frequency. Asnoted above, pulse amplitude may comprise pulse voltage or pulsecurrent. Preferably, each of these parameters may be independentlyvaried, as specified by control law output signal U 997 generated bycontrol circuit 72. As noted, the stimulus waveforms comprising theneuromodulation signal (NMS) generated by output stage circuit 77 areapplied to patient through intracranial (IC) stimulating electrode array37. Pulse generator 73 generates a single waveform when single channelstimulation is to be used, and a plurality of waveforms when multiplechannel stimulation is to be used. It may generate monophasic orbiphasic waveforms.

In one preferred embodiment, charge balanced biphasic waveforms areproduced. Those skilled in the art are aware that the net chargecontained in a given pulse is given by the time integral of the stimuluscurrent over the duration of the pulse. In a biphasic configuration, apair of pulses of opposite polarity is generated, and the pulse currentamplitude and pulse width are chosen such that the charge amplitude isequal in magnitude and opposite in polarity. In some cases, it isdesirable for the pulses comprising the biphasic pulse pair to havedifferent amplitudes; in this case, the pulse widths are chosen toinsure equal and opposite charges so the pulse par introduces zero netcharge to the neural tissue 250. The capability to deliver pulse pairswith balanced charges is yet a further advantage conferred by thecurrent regulation mode described above.

Even though the waveform parameters of the pulse pairs are calculated todeliver a zero net charge, in practice, noise and precision limitationsin computation and resolution limitations and nonlinearities in thedigital to analog conversion and amplification stages may result inslight imbalances in the pulse pair charges. Over time, this can resultin the delivery of a substantial accumulated net charge to the neuraltissue. To eliminate this potential for net charge delivery to neuraltissue, a direct current (DC) blocking capacitor is employed. This is atechnique that is well known to those or ordinary skill in the art. Inone preferred embodiment, a DC blocking capacitor is included withinmultiplexor 75 in series with stimulator output path 111.

Typically, multi-channel stimulation is used in the case of bilateralstimulation. Since the disease progression is typically asymmetrical,and the normal motor control systems governing movement on the left andright side of the body are also highly independent of each other, thedelivery of treatment to the left and right sides of the body should becontrolled separately. This represents one need for a multiple channelneuromodulation signal (NMS) 998. Multichannel stimulation is alsoexpected to be beneficial in treating patients with variable involvementof different limbs. For example, the magnitude neuromodulation of aportion of the globus pallidus required to achieve optimal controls ofarm tremor may be different from the optimal level of neuromodulation ofseparate portion of the globus pallidus to achieve optimal control ofleg tremor. In this case, separate electrodes or electrode pairs arerequired to deliver optimal levels of neuromodulation to control tremorin these two regions of the body. Correspondingly, these separateelectrodes or electrode pairs will be driven by separate neuromodulationsignal (NMS) channels, necessitating a multichannel system.

A further need for multichannel neuromodulation signal (NMS) is thecontrol of multiple symptoms of the movement disorder and the sideeffects arising from pharmacologic treatment. Optimal control of tremor,dyskinesias, and rigidity are not achieved by modulation of the samesite at the same intensity. For this reason, multiple and separatelycontrolled channels of neuromodulation are required to simultaneouslyachieve optimal control of these multiple symptoms and side effects.Each of these symptoms and side effects may be considered to compriseone or more element in a multivariable disease state. A multivariablecontrol system will be required to optimally drive each of these diseasestate elements to its desired value, ideally toward a target minimumlevel and thus achieve optimal control of this multiplicity of diseasestates. This multivariable control system may be implemented as multipleindependent control laws each with separate though potentiallyoverlapping sensory inputs or as a multivariable control law matrix.

Stimulation via each of the multiple channels comprising theneuromodulation signal (NMS) 998 is characterized by separate thoughpossibly overlapping sets of one or more of the following parameters:stimulation voltage, stimulation current stimulation frequency of pulseswithin the same burst, frequency of bursts, pulse width, pulses perburst, duration of burst, and interpulse interval. The stimuluswaveforms are amplified by output amplifier 74 to generate an amplifiedstimulus waveform. Specifically, pulse generator 73 transfersinformation to output amplifier 74 which includes information thatuniquely specifies the desired stimulation waveform. In a preferredembodiment, the information is in the form of an analog signal whichrepresents a scaled version of the voltage or current waveform to bedelivered to the tissue. It should be understood that other forms of thesignal generated by pulse generator 73 may be used, includingcombinations of at least one of analog and digital representations.Output amplifier 74 performs amplification and regulation of thereceived stimulus waveform generated by the pulse generator 73. This maybe regulation of electrical current to achieve desired voltage orregulation of electrical voltage to achieve desired current, dependingon whether a voltage or current waveform is to be delivered to thenervous system component.

As one skilled in the relevant art would find apparent, voltageregulation is simpler to implement, and is a technique which is commonlyused by many conventional stimulators. Current regulation, on the otherhand, is more complex but allows for more precise control of the appliedstimulation. Current regulation insures that a specified amount ofcurrent is delivered, regardless of the impedance of the electrode.Current regulation is advantageous in that it allows for precise controlof stimulation level despite changes in electrode impedance whichinvariably occur over time. Since electrode impedances often change,typically increasing as they become encapsulated by fibrosis, currentregulation is preferred to avoid the decrease in current which wouldoccur if voltage regulation were to be used in such circumstances.

The amplified stimulus waveform generated by output amplifier 74 isconducted along stimulator amplifier output path 112 to multiplexor 75.Multiplexor 75 allows for delivery of a stimulating electrode outputsignal (SEOS) to the intracranial stimulating electrode array 37,multiplexed with sensing of a stimulating electrode input signal (SEIS).Specifically, multiplexor 75 serves to alternately connect intracranialstimulating electrode (ICSE) array 37 to output amplifier 74 andintracranial stimulating electrode amplifier 57. Connection ofintracranial stimulating electrode (ICSE) array 37 to output amplifier74 facilitates delivery of neural modulation signal to neural tissue,while connection of intracranial stimulating electrode (ICSE) array 37to intracranial stimulating electrode amplifier 57 facilitatesmonitoring of neural activity in the region being stimulated.

Multiplexor 75 allows delivery of neural modulation signals to neuraltissue concurrent with monitoring of activity of same neural tissue;this facilitates real-time monitoring of disease state and response totreatment. Stimulating electrode output signal (SEOS) from outputamplifier 74 is conducted along stimulator amplifier output path 112 tomultiplexor 75. Multiplexor 75 conducts output from output amplifier 74to stimulator output path 111 which conducts the stimulating electrodeoutput signal to intracranial stimulating electrode array 37. Tofacilitate periodic sampling of neural activity in tissue beingstimulated, multiplexor 75 alternatively conducts signal arising fromstimulated tissue via intracranial stimulating electrode array (ICSE) 37and stimulator output path 111 to multiplexed stimulator recording inputpath 113 and intracranial stimulating electrode amplifier 57.

Multiplexor 75 selectively conducts the signal on multiplexed stimulatorrecording input path 113 to amplifier 57. Multiplexor 75 may alternateconduction between path 111 and path 112 or path 113 using temporalmultiplexing, frequency multiplexing or other techniques to allowconcurrent access to the intracranial stimulating electrode (ICSE) array37 for modulation of tissue activity and monitoring of tissue activity.Temporal multiplexing is a well known technique and frequencymultiplexing of stimulation and recording signals in known to thoseskilled in the art. In this embodiment, temporal multiplexing isaccomplished by alternately connecting stimulator output path 111 tostimulator amplifier output path 112 and multiplexed stimulatorrecording input path 113. In one embodiment, frequency multiplexing isaccomplished by passing a band-limited portion of stimulating electrodeoutput signal SEOS via the stimulator output path 111 to intracranialstimulating electrode array 37 while simultaneously monitoring activityon intracranial stimulating electrode array 37 within a separatefrequency band, thereby generating a stimulating electrode input signalSEIS. Thus, stimulating electrode input signal SEIS is conducted fromthe intracranial stimulating electrode array 37 to stimulator outputpath 111 to multiplexor 75 and via multiplexed stimulator recordinginput path 113 to intracranial stimulating electrode array amplifier 57.

Multiplexor 75 facilitates conduction between stimulator amplifieroutput path 112 and multiplexed stimulator recording input path 113 toallow automated calibration. In this mode, a calibration signal of knownamplitude is generated by pulse generator 73 and amplified by outputamplifier 74 which, for calibration purposes, delivers a voltageregulated signal via stimulator amplifier output path 112 to multiplexor75. Multiplexor 75 conducts amplified calibration signal to multiplexedstimulator recording input path 113 which conducts signal tointracranial stimulating electrode amplifier 57.

Although not included in the illustrative embodiment, multiplexed orintermittent connection of stimulator amplifier output path 112 to theinputs of at least on of the other amplifiers, including EMG amplifier59, EEG amplifier 60, accelerometer amplifier 61, acoustic amplifier 62,peripheral nerve electrode amplifier 63, and intracranial recordingelectrode amplifier 58, may be implemented without departing from thepresent invention. The same multiplexed connections may be used tocalibrate the pulse generator 73 and output amplifier 74.

Referring to FIG. 15, an analog switch may be used to connect one or anopposing polarity pair of Zener diodes across the noninverting andinverting inputs of intracranial recording electrode amplifier 58. Inthis configuration, the Zener diodes would limit the maximal amplitudeof the calibration signal in one or both polarities to known values,allowing for accurate calibration of intracranial recording electrodeamplifier 58. The analog switch may then be deactivated, removing thecathode of the single or pair of Zener diodes from the input ofintracranial recording electrode amplifier 58 to allow measurement ofstimulating electrode output signal (SEOS) for calibration of pulsegenerator 73 and output amplifier 74. This is described in greaterdetail below.

Multiplexor 75 also facilitates conduction between stimulator amplifieroutput path 112, multiplexed stimulator recording input path 113, andstimulator output path 111 to allow measurement of impedances ofcomponents of intracranial stimulating electrode array 37. In thiselectrode impedance measurement mode, a three way connection betweenstimulator amplifier output path 112, multiplexed stimulator recordinginput path 113, and stimulator output path 111 is created. When outputamplifier 74 is operated in current regulated mode, it delivers an SEOSof known current via stimulator output path 111 to intracranialstimulating electrode array 37. The voltages generated across theelements of intracranial stimulating electrode array 37 generally arethe products of the electrode impedances and the known stimulatingcurrents. These voltages are sensed as the stimulating electrode inputsignal SEIS by the intracranial stimulating electrical amplifier 57.

Reference module 116 contains memory registers in which control lawreference values are stored. Such reference values include but are notlimited to target disease state levels, target symptom levels, includingtarget tremor level, and threshold levels. Threshold levels include butare not limited to disease and symptom levels, including tremorthreshold levels. Neural modulation amplitude may be increased when atleast one of disease state and symptom level exceed the correspondingthreshold. Similarly neural modulation amplitude may be decreased orreduced to zero when either the disease state or symptom level fallsbelow the corresponding threshold.

Reference module 116 is connected to patient interface module 55,facilitating both monitoring and adjustment of reference values bypatient. Reference module 116 is also connected to supervisory module56, facilitating both monitoring and adjustment of reference values byphysician or other health care provider. Supervisory module 56 may beused by the neurologist, neurosurgeon, or other health careprofessional, to adjust disease state reference R values for the one ormore control laws implemented in control circuit 72. The disease statereference R values specify the target level at which the correspondingdisease states are to be maintained, as quantified by the disease stateestimate X values, providing reference values for control lawsimplemented in control law circuit block 231 (FIG. 11; discussed below)and contained within control circuit 72. Reference module 116 may alsoreceive input from control circuit 72, facilitating the dynamicadjustment of reference disease state “r” (discussed below). Referencemodule 116 may additionally receive input from disease state estimatormodule array (DSEMA) 229 (FIG. 11; discussed below) and aggregatedisease state estimator 195 (FIG. 11; discussed below) and components ofsignal processor 71, for use in dynamically determining referencedisease state “r”.

FIG. 10 is a schematic diagram of signal processor 71. In thisillustrative embodiment, signal processor 71 includes a disease stateestimator module array 229 that includes one or more signal processormodules that generate a quantitative estimate of at least one diseasestate or parameter thereof based upon its respective input. For example,magnitude of tremor in the 3 to 5 Hz range represents one possiblerepresentation of a disease state. This could be an absolute ornormalized quantification of limb acceleration in meters per secondsquared. This component of the disease state would be calculated almostexclusively from sensory feedback from accelerometer array 52. Anotherpossible disease state is the frequency of occurrence of episodes oftremor activity per hour. This element of the disease state may beestimated from any of several of the sensory feedback signals. In thiscase, the most accurate representation of this disease state element isobtained by applying a filter such as a Kalman filter to calculate thisparameter based upon a weighted combination of the sensory feedbacksignals. Such weighting coefficients are calculated from quantifiedmeasures of the accuracy of and noise present upon each sensory feedbackchannel.

In the illustrative embodiment, disease state estimator module array 229includes an EMG signal processor 233, EEG signal processor 234,accelerometer signal processor 235, acoustic signal processor 236,peripheral nerve electrode (PNE) signal processor 237, intracranialrecording electrode (ICRE) signal processor 238, and intracranialstimulating electrode (ICSE) signal processor 239. It should beunderstood that other signal processors may also be included in thearray 229. Inputs to these modules include conditioned EMG signal path78, conditioned EEG signal path 79, conditioned accelerometer signalpath 80, conditioned acoustic signal path 81, conditioned peripheralnerve electrode (PNE) signal path 82, conditioned intracranial recordingelectrode (ICRE) signal path 83, and conditioned intracranialstimulating electrode (ICSE) signal path 84, respectively. Communicationbetween these modules is facilitated. The output(s) of each of themodules is connected to an aggregate disease state estimator 195.Aggregate disease state estimator 195 generates a single or plurality ofdisease state estimates “X” indicative of state of disease and responseto treatment.

In the preferred embodiment, the acceleration of at least one of theaffected limb and the head, each of which is sensed as a sensoryfeedback channel by an element of the accelerometer array 52, serves asrespective elements in the disease state estimate X. These elements ofdisease state estimate X are inputs to respective control lawsimplemented in control circuit 72. of input to the control law. Acontrol law governing the function of a proportional controller usingacceleration as its single sensory feedback channel is given by equation(1):u ₁=0.3166(V*s ² /m)*ACC  (1)and ifu ₂=0.6333(V*s ² /m)*ACC  (2)

where u₁ and u₁ are the stimulation voltage given in volts; and ACC isthe limb, mandible, or head acceleration given in meters per secondsquared (m/s²).

In equation (1), the stimulation site is the ventroposterolateralpallidum, the output stage mode is voltage regulated, the waveform is acontinuous train of square waves, the amplitude u₁ is given in volts(typically approximately 1 volt), and the remaining stimulationparameters include a pulse width of 210 microseconds, and a stimulationfrequency of 130 Hz. In equation (2), the stimulation site is theventral intermediate thalamic nucleus (Vim), the output stage mode isvoltage regulated, the waveform is an intermittent train of square waveswith an on time of 5 minutes and an off time of 45 seconds, theamplitude u₂ is given in volts (typically approximately 3 volts), andthe remaining stimulation parameters include a pulse width of 60microseconds, and a stimulation frequency of 130 Hz.

In one preferred embodiment, the ACC signal represents the averageacceleration over a finite time window, typically 15 to 60 seconds. Thiseffective lowpass filtering provides a stable sensory feedback signalfor which a proportional control law is appropriate. If stability andperformance requirements dictate, as is familiar to those practiced inthe art of feedback control, other components, including an integratorand a differentiator may be added to the control law to produce aproportional-integral-differential (PID) controller, as needed.

One preferred embodiment also includes electromyographic (EMG) signalsas sensory feedback in the calculation of at least one element of thedisease state estimate X which is an input to the control law. Asdiscussed in the section describing EMG signal processor 233, the EMGsignals are rectified by full wave rectifier 123, passed throughenvelope determiner 124, passed through several bandpass filters 125,127, 129, 131, 133 and associated threshold discriminators 126, 128,130, 132, 134 and then passed in parallel to each of integrator 135 andcounter 136. Integrator 135 generates an output which is a weightedfunction of its inputs and represents the average magnitude of tremoractivity over a given time window −w/2 to +w/2. A simplifiedrepresentation of this is given by equation (3):U ₃ =∫X _(EMG) −dt  (3)

over a given time window −w/2 to +w/2.

As is familiar to those skilled in the art of control theory, anintegral controller is marginally stable. To confer stability to thiscontrol law, the equivalent of a finite leak of the output magnitude u₄to zero is added to maintain stability. A more general form of thisequation is given by equation (4):−C ₁ ∂u ₄ /dt+C ₂ ·u ₄ =B ₁ ·∂X _(EMG) /dt+B ₂ *X _(EMG)  (4)

Shown as a system function, the control law output U is given as theproduct of a transfer function H(s) and the disease estimate X, theinput to the control law:u(s)(C ₁ ·s+C ₂)=X _(EMG)(s)(B ₁ ·s+B ₂)  (5)u(s)/X _(EMG)(s)=(B ₁ ·s+B ₂)/(C ₁ ·s+C ₂)  (6)H(s)=u(s)/X _(EMG)(s)=(B ₁ ·s+B ₂)/(C ₁ ·s+C ₂)  (7)

One such control law with an appropriate time response is given by:H(s)=u(s)/X _(EMG)(s)=G _(VIEMG)(0.1*s+1)/(2·s+1)  (8)

where G_(V/EMG) is the gain in neuromodulation signal (NMS) (volts pervolt of EMG signal).

For intramuscular EMG electrodes, signal amplitudes are on the order of100 microvolts. For neuromodulation signal (NMS) parameters of 2 voltsamplitude, 60 microseconds pulse width, 130 Hz stimulation frequency,the appropriate overall gain G′_(V/EMG) is 20,000volts_(NMS)/volts_(EMG). Since the preamplifier stage performsamplification, 1000, in the preferred embodiment, the actual value forG_(V/EMG) as implemented in the control law is 20volts_(NMS)/volts_(PREAMPL EMG).

Disease state estimator 195 determines estimates of disease stateincluding but not limited to long-term, or baseline, components,circadian components, postprandial components, medication inducedalleviation of components, medication induced components, and futurepredicted behavior of said components. Output of disease state estimator195 includes output of observer 228, depicted in FIG. 11, which makesuse of an adaptive model of disease behavior to estimate disease stateswhich are not directly detectable from sensors. Such sensors provideinput to the adaptive model to correct state estimates and modelparameters. Each of the signal processor modules in disease stateestimator module array 229 are described below.

FIG. 3 is a block diagram of intracranial recording electrode (ICRE)signal processor 238 and intracranial stimulating electrode (ICSE)signal processor 239, each of which are included within signal processor71 in the illustrative embodiment illustrated in FIGS. 2 and 10. ICREsignal processor module 238 and ICSE signal processor module 239 processsignals from one or more intracranial electrodes, including but notlimited to those comprising intracranial recording electrode array 38and intracranial stimulating electrode array 37. As noted, intracranialstimulating electrode array 37 is comprised of one or more intracranialstimulating electrodes while intracranial recording electrode array 38is comprised of one or more intracranial recording electrodes.

Input to ICRE signal processor 238 is conditioned intracranial recordingelectrode (ICRE) signal path 83 noted above. This input is connected toa spike detector 85 which identifies action potentials. Spike detectiontechniques are well known to those skilled in the art and generallyemploy low and high amplitude thresholds. Waveforms having amplitudesgreater than the low threshold and lower than the high threshold aredetermined to be action potentials. These thresholds may bepredetermined or adjusted manually using supervisory module 56 or may beadapted in real-time by an algorithm which sweeps the threshold througha range of values to search for values at which action potential spikesare consistently recorded. The low amplitude threshold is set above theamplitude of background noise and that of nearby cells not of interest,and the high amplitude threshold is set above the amplitude of thedesired action potentials to allow their passage while eliminatinghigher amplitude noise spikes, such as artifacts arising from electricalstimulation currents. Bandpass, notch, and other filtering techniquesmay also be used to improve signal to noise ratio and the sensitivityand specificity of spike detectors. Individual neuron action potentialsare usually recorded using fine point high-impedance electrodes, withimpedances typically ranging from 1 to 5 megohms. Alternatively, largerlower-impedance electrodes may be used for recording, in which case thesignals obtained typically represent aggregate activity of populationsof neurons rather than action potentials from individual neurons. Spikedetector 85 passes the waveform(s) to a spike characterizer 86. Spikecharacterizer 86 determines firing patterns of individual neurons. Thepatterns include, for example, tonic activity, episodic activity, andburst fining. Spike characterizer 86 calculates parameters thatcharacterize the behavior of the individual and groups of neurons, theactivity of which is sensed by intracranial recording electrode array38. In one embodiment, the characterization includes parameterization ofrecorded action potentials, also referred to as spikes, bursts ofspikes, and overall neural activity patterns. This parameterizationincludes, but is not limited to, calculation of frequencies of spikes,frequencies of bursts of spikes, inter-spike intervals, spikeamplitudes, peak-to-valley times, valley-to-peak times, spectralcomposition, positive phase amplitudes, negative phase amplitudes, andpositive-negative phase differential amplitudes. These parameters aredepicted in FIG. 14 and are discussed below. Based on theseparameterization, spike characterizer 86 discriminates individual spikesand bursts originating from different neurons. This discriminationfacilitates serial monitoring of activity of individual and groups ofneurons and the assessment and quantification of activity change,reflective of change in disease state and of response to therapy.

A spike analyzer 87 receives as input the parameters from spikecharacterizer 86. Spike analyzer 87 extracts higher level information,including but not limited to average spike frequencies, averageinterspike intervals, average amplitudes, standard deviations thereof,trends, and temporal patterning. By comparing current spike frequencyrates to historical spike frequency data, spike analyzer 87 additionallycalculates the rates of change of spike parameters. Prior trends andcurrent rates of change may then be used to predict future behaviors.Rates of change of the parameters include but are not limited toautocorrelation and digital filtering.

Spike analyzer 87 may receive additional input from accelerometers,including but not limited to at least one of head mounted accelerometer12, proximal accelerometer 28, enclosure mounted accelerometer 36, anddistal accelerometer 33. Spike analyzer 87 may receive indirect inputfrom accelerometers, such as from conditioned or processed signalsarising therefrom. This may include, for example, the signal transmittedby conditioned accelerometer signal path 80.

Spike analyzer 87 may also receive additional input from EMG arrays 50,such as a proximal EMG electrode array 45, enclosure-mounted EMGelectrode array 46, or distal EMG electrode array 47. Spike analyzer 87may receive indirect input from such EMG electrode arrays 50, such asfrom conditioned or processed signals arising therefrom, including butnot limited to the signal transmitted by conditioned EMG signal path 78.

These additional inputs from accelerometers and EMG arrays facilitatesthe characterization of neuronal firing patterns relative to activity ofmuscle groups and movement of joints, including but not limited tocharacterization of neuronal spike amplitudes and tuning of firing tomovement, including but not limited to movement velocity and direction.The characterization may be used to assess functioning of thesensorimotor system, including but not limited to motor response time,and to measure the disease state and response to therapy.

Intracranial recording electrode (ICRE) single unit-based (SU) diseasestate estimator 88 receives input from spike characterizer 86 and/orspike analyzer 87. Spike analyzer 87 provides higher level information,including but not limited to average spike frequencies, averageinterspike intervals, average amplitudes, standard deviations thereof,trends, and temporal patterning to disease state estimator 88. Theseinputs are representative of the current neuronal activity in the tissuefrom which the intracranial recording electrodes (ICRE) are recording.ICRE SU disease state estimator 88 may also receive input representativeof one or more signals, including desired neuronal activity, fromcontrol circuit 72. The ICRE SU disease state estimate X_(ICRE) _(—) SUcalculated by ICRE SU disease state estimator 88, may be comprised of asingle or a plurality of signals, consistent with a representation ofthe disease state by a single or a multitude of state variables,respectively. The ICRE MU disease state estimate X_(ICRE) _(—) _(MU)calculated by ICRE MU disease state estimator 88, may be comprised of asingle or a plurality of signals, each representative of multiunitneurophysiological signals, i.e. reflective of concurrent activity ofnumerous neurons. Both ICRE SU disease state estimate X_(ICRE) _(—)_(SU) and ICRE MU disease state estimate X_(ICRE) _(—) _(MU) are outputto aggregate disease state estimator 195.

Referring to FIG. 3, conditioned intracranial recording electrode (ICRE)signal path 83 additionally connects to filter 101. Filter 101 ispreferably of the bandpass type filter. In one embodiment, the bandpassfilter 101 has a passband of 0.1 to 100 Hz, although other ranges may beused. Output of filter 101 connects to spectral energy characterizer102, which may be implemented in any of several hardware or softwareforms. For example, in one embodiment, the spectral energy characterizer102 is implemented using real-time fast Fourier transform (FFT)techniques. Alternatively, other digital or analog techniques may alsobe used.

It should be understood that inputs and outputs from spike detector 85,spike characterizer 86, spike analyzer 87, disease state estimator 88,filter 101, spectral energy characterizer 102, spectral energy analyzer103, and disease state estimator 104 may be comprised of individualsignals or a plurality of signals. Further, spike detector 85, spikecharacterizer 86, spike analyzer 87, disease state estimator 88, filter101, spectral energy characterizer 102, spectral energy analyzer 103,and disease state estimator 104 may each have different parameters andsignal processing characteristics for each of the multiple signalsprocessed. Because baseline neuronal firing rates differ among variousanatomical and functional regions of the brain, and their involvement indisease states and susceptibility to change in firing patterns varies,the respective signal processing circuitry and logic will varycorrespondingly. For example, baseline firing rates among neurons in theglobus pallidus externus are approximately 43 Hz and those in the globuspallidus internus are 59 Hz.

The input to intracranial stimulating electrode ICSE signal processor239, referred to above as conditioned intracranial stimulating electrode(ICSE) signal path 84, connects to spike detector 89. Spike detector 89identifies action potentials in a manner similar to that described abovewith reference to spike detector 85. Intracranial stimulating electrodeICSE signal processor 239 performs a similar set of functions asintracranial recording electrode ICRE signal processor 238 on adifferent set of sensory feedback signals. As noted above, spikedetection techniques are well known to those skilled in the art.

Spike detector 89 passes waveforms to spike characterizer 90, which useswell known techniques to calculate parameters than characterize thebehavior of the individual and groups of neurons, the activity of whichis sensed by intracranial stimulating electrode array 37. As noted abovewith respect to spike characterizer 86, this characterization mayinclude parameterization of spikes, bursts of spikes, and overall neuralactivity patterns. Similarly, the parameterization may includecalculation of spike frequencies, burst frequencies, inter-spikeintervals, amplitudes, peak-to-valley times, valley-to-peak times,spectral composition, positive phase amplitudes, negative phaseamplitudes, and positive-negative phase differential amplitudes. Suchcharacterization of neural spikes is known to those skilled in the artof neurophysiology. Based on this parameterization, spike characterizer90 discriminates individual spikes and bursts originating from differentneurons. As noted, such discrimination facilitates serial monitoring ofactivity of individual and groups of neurons and the assessment andquantification of activity change, reflective of change in disease stateand of response to therapy.

Spike analyzer 91 receives the parameters from spike characterizer 90,and extracts higher level information, including average spikefrequencies, average interspike intervals, average amplitudes, standarddeviations thereof, trends, and temporal patterning. The function andoperation of spike analyzer 91 is similar to that described herein withreference to spike analyzer 87. Similarly, spike analyzer 91 may receiveadditional input directly or indirectly from accelerometers and/or EMGarrays to facilitate the characterization of neuronal firing patternsrelative to activity of muscle groups and movement of joints. This mayinclude, for example, characterization of neuronal spike amplitudes andtuning of firing to movement, including but not limited to movementvelocity and direction. Such characterization may be used to assesfunctioning of the sensorimotor system, including but not limited tomotor response time, and to measure the disease state and response totherapy.

Intracranial stimulating electrode (ICSE) single unit-based (SU) diseasestate estimator 92 receives input from either or both spikecharacterizer 90 and spike analyzer 91. ICSE SU disease state estimator92 receives input representative of the current neuronal activity fromspike characterizer 90. ICSE SU disease state estimator 92 may receiveinput representative of at least one of several signals, includingdesired neuronal activity, actual neuronal activity, and the differencebetween these quantities. The ICSE SU disease state estimate, calculatedby ICSE SU disease state estimator 92, may be comprised of a single or aplurality of signals, consistent with a representation of the diseasestate by a single or a multitude of state variables, respectively.

As with intracranial recording electrode signal processor 238, inputsand outputs from spike detector 89, spike characterizer 90, spikeanalyzer 91, disease state estimator 92, filter 106, spectral energycharacterizer 107, spectral energy analyzer 108, and disease stateestimator 109 may include individual or a plurality of signals, and eachmay have different parameters and signal processing characteristics foreach of the multiple signals processed. Because baseline neuronal firingrates differ among various anatomical and functional regions of thebrain, and their involvement in disease states and susceptibility tochange in firing patters varies, the respective signal processingcircuitry and logic varies correspondingly.

FIG. 4 is a schematic diagram of a globus pallidus 119 implanted withstimulating and recording electrodes. Intracranial catheter 7 is shownin place with electrode of the intracranial stimulating electrode array37 located within the globus pallidus internus (Gpi) 120, includingglobus pallidus internus internal segment (GPi,i) 94 and globus pallidusinternus external segment (GPi,e) 95, and globus pallidus externus (GPe)96.

Intracranial stimulating electrodes 1 and 2 are shown implanted in theglobus pallidus internus internal segment (GPi,i) 94; and intracranialstimulating electrodes 3 and 4 are shown implanted in the globuspallidus internus external segment (GPi,e) 95 and globus pallidusexternus (GPe) 96, respectively. It should be understood that thisarrangement is illustrative of one preferred embodiment, and otherstimulating and recording electrode configurations may be employedwithout departing from the present invention.

The optic tract 97 is shown in its close anatomical relationship to theglobus pallidus internus (Gpi) 120. The risk inherent in treatmentmodalities involving irreversible tissue ablation should be apparent;stereotactic errors of only one to several millimeters during lesioningof the globus pallidus internus (Gpi) 12 o may result in irreversibledamage or complete destruction of the optic tract 97. Furthermore, theadvantage of a system which dynamically adjusts the amplitude ofinhibitory electrical stimulus to the globus pallidus 119 to minimizesaid amplitude offers the potential advantage of minimization of sideeffects including interference with visual signals of the optic tract 97and prevention of overtreatment.

Intracranial stimulating electrodes 1,2,3,4 are shown implanted in theGPi,i 94, GPi,e 95, GPe 96, respectively. This is one preferredembodiment. Numerous permutations of electrode stimulation siteconfiguration may be employed, including more or fewer electrodes ineach of these said regions, without departing from the presentinvention. Electrodes may be implanted within or adjacent to otherregions in addition to or instead of those listed above withoutdeparting from the present invention, said other reasons including butnot limited to the ventral medial Vim thalamic nucleus, other portion ofthe thalamus, subthalamic nucleus (STN), caudate, putamen, other basalganglia components, cingulate gyrus, other subcortical nuclei, nucleuslocus ceruleus, pedunculopontine nuclei of the reticular formation, rednucleus, substantia nigra, other brainstem structure, cerebellum,internal capsule, external capsule, corticospinal tract, pyramidaltract, ansa lenticularis, white matter tracts, motor cortex, premotorcortex, supplementary motor cortex, other motor cortical regions,somatosensory cortex, other sensory cortical regions, Broca's area,Wernickie's area, other cortical regions, other central nervous systemstructure, other peripheral nervous system structure, other neuralstructure, sensory organs, muscle tissue, or other non-neural structure.

Referring to FIGS. 3 and 4, a small percentage of cells in the globuspallidus internus internal segment 94 and globus pallidus internusexternal segment 95 exhibit tremor-synchronous discharges. A s noted, atleast one of single unit recordings from individual cells and multipleunit recordings from a plurality of cells are processed by signalprocessor 71. The single and multiple unit recordings may be derivedfrom signals arising from intracranial stimulating electrode array 37,intracranial recording electrode array 38, or other sources. The outputfrom signal processor 71 is connected to control circuit 72 and theoutput may represent at least one of disease state, magnitude ofsymptomatology, response to therapy, other parameter, and combinationthereof.

Individual electrodes comprising intracranial stimulating electrodearray 37 and intracranial recording electrode array 38 may each be ofthe microelectrode type for single unit recordings, macroelectrode typefor multiple unit recordings, other electrode type, or a combinationthereof, without departing from the spirit of the present invention. Inone preferred embodiment, intracranial stimulating electrode array 37consists of macroelectrodes. The macroelectrodes facilitate delivery ofstimulation current at a lower charge density (coulombs per unit ofelectrode surface area) than microelectrodes of the same chemistry andsurface treatment. The dimensions of intracranial stimulating electrodes1-4 are selected such that the current density, or electrical currentdivided by electrode surface area, is below the threshold of reversiblecharge injection for the given electrode material.

Standard single cell recording technique, using an electrode with animpedance of typically 1-2 Megohms, involves bandpass filtering with −6decibel (dB) points at 300 and 10,000 Hertz. This filtering, or amodification thereof, may be accomplished by ICRE filter 65 and ICSEfilter 64; alternatively, it may be performed in spike detector 85 andspike detector 89, respectively, or other portion of stimulating andrecording circuit 26.

FIG. 5 is a block diagram of one embodiment of an EMG signal processor233 which is included in a preferred embodiment of signal processor 71.EMG signal processor 233 processes signals from EMG electrode array 50,performing functions including but not limited to full waverectification, envelope determination, bandpass filtering, thresholddiscrimination, and others described in more detail below, to producesignals indicative of the overall magnitude of tremor as well as thefrequency at which tremor episodes occur. As noted, EMG electrode array50 includes, but is not limited to, proximal EMG electrode array 45,enclosure-mounted EMG electrode array 46, and distal EMG electrode array47. EMG electrodes may be located in any implanted or external locationwithout departing from the present invention. For example, electrodesmay be located within or in proximity to the hand, forearm, arm foot,calf, leg, abdomen, torso, neck, head, haw, lip, eyelid, larynx, vocalcords, and tongue.

Conditioned EMG signal path 78 is also connected to a well-known fullwave rectifier 123 now or later developed. Output from the full waverectifier 123 is coupled to an input of an envelope determiner 124.Determination of the envelope of a modulated signal is well known tothose skilled in the art of electronics; this may be readily implementedin analog or digital hardware or in software. Output of envelopedeterminer 124 is connected to inputs of filters 125, 127, 129, 131 and133. In one embodiment, filters 125, 127, 129, 131, 133 implementpassbands of approximately 0.1-2 Hz, 2-3 Hz, 3-5 Hz, 7-8 Hz, and 8-13Hz, respectively. Outputs of filters 125, 127, 129, 131 and 133 areconnected to threshold discriminators 126, 128, 130, 132, 134,respectively.

Threshold discriminators 126, 128, 130, 132, and 134 generate outputsrepresenting episodes of normal voluntary movement (Mv), low frequencyintention tremor (Til) resting tremor (Tr), high frequency intentiontremor (Tih), and physiologic tremor (Tp), respectively. These outputsare each connected to both of integrator 135 and counter 136. Integrator135 generates outputs representative of the total activity of each ofthe above types of movement over at least one period of time. One suchtime period may be, for example, time since implantation, time sincelast visit to physician or health care provider, month internal, weekinterval, day interval, interval since last medication dose, intervalsince last change in stimulation parameters, weighted average ofmultiple time windows, and convolution of said activity with arbitrarytime window function.

Counter 136 generates outputs representative of the number of episodesof each of the above types of movement over at least one period of time.Such period of time may be, for example, time since implantation, timesince last visit to physician or health care provider, month interval,week internal, day interval, interval since last medication dose,interval since last change in stimulation parameters, and weightedaverage of said number of episodes over multiple time windows. Outputsfrom integrator 135 and counter 136 are connect to EMG analyzer 137. EMGanalyzer 137 performs a number of functions including, for example,calculation of proportions of tremor activity which are of the rest andthe intention type, ratios of different types of tremor activity, thelevel of suppression of resting tremor activity with voluntary movement,assessment of temporal patterns of EMG activity. EMG disease stateestimator 138 receives inputs from EMG analyzer 137 and generates outputrepresentative of disease state based upon said input. In one preferredembodiment, two disease states are calculated, including a signalrepresentative of the overall magnitude of tremor activity and a signalrepresentative of the frequency of occurrence of tremor events. Itshould be understood that all signals paths may transmit one or moresignals without departing from the present invention.

EMG signals may be sensed from any individual or group of muscles andprocessed in a manner including but not limited to the determination ofseverity and frequency of occurrence of various tremor types. Normal orphysiologic tremor includes movement in the 8-13 Hz range and may beused as a normalization for the other types of sensed tremor. Thepredominant pathological form of tremor exhibited in Parkinson's diseasepatients is the classical “resting” tremor which includes movements inthe 3-5 Hz range which are present at rest and suppressed in thepresence of voluntary movement. In the present invention, quantificationof this tremor type serves as a heavily weighted sensory input in theassessment of disease state and response to therapy. Parkinson's diseasepatients may also exhibit intention tremor, of which there are twotypes. The first type of intention tremor is referred to as “lowfrequency intention tremor” (Til in the present invention) and consistsof movements in the 2-3 Hz range. A second type of intention tremor isreferred to as “high frequency intention tremor” Tih in the presentinvention and consists of irregular movements in the 7-8 Hz range whichpersist throughout voluntary movement. Other types of tremor havingassociated movement in other ranges may be sensed and represented by theEMG signals.

EMG signals from at least one of orbicularis oculi (effecting eyeclosure), levator palpebrae (effecting eye opening), and other musclescontributing to eyelid movement, may be sensed and processed todetermine frequency of eye blinking. Patients with Parkinson's diseaseexhibit a reduction in eyeblinking frequency from the normal of 20 perminute to 5 to 10 per minute, and this parameter is sensed as a measureof disease severity and response to treatment. Additionally, said EMGsignals may be sensed and processed for detection and quantification ofblepharoclonus, or rhythmic fluttering of the eyelids, and used as ameasure of disease state and response to therapy. EMG signals, includingbaseline levels thereof, may be used to quantify rigidity and hypertonusas measures of disease state and response to therapy. Discharge patternsof individual motor units, including but not limited to synchronizationof multiple units and distribution of intervals preceding and followingdischarge, may be used as measures of disease state and response totherapy.

FIG. 6 is a block diagram of one embodiment of an EEG signal processormodule 234 which is included in embodiments of signal processor 71. TheEEG signal processor module 234 processes signals from EEG electrodearray 51. Conditioned EEG signal path 79 connects to an input ofartifact rejecter 139 which rejects signals with amplitudes above athreshold. In one embodiment, this threshold is 0.1 mV. An output fromartifact rejecter 139 connects to an input of each of supplementarymotor area signal extractor 140 and filters 143, 146, 149, 152, 219.Filters 143, 146, 149, 152, and 219 are preferably of the bandpass typewith passbands of 13-30 Hz, 8-13 Hz, 4-7 Hz, 0.1-4 Hz, and 0.1-0.3 Hz,respectively. Each filter output is connected to an input of anassociated full wave rectifier 141, 144, 147, 150, 153, 220. Each fullwave rectifier 141, 144, 147, 150, 153, 220 is connected to an input ofan associated envelope determiner 142, 145, 148, 151, 154, and 221,respectively. The envelope determiners generate a signal representativeof the envelope of the input signal, typically performed by lowpassfiltering with a time constant of 5 seconds. Finally, outputs ofenvelope determiners 142, 145, 148, 151, 154, and 221 are connected toEEG disease state estimator 155.

Signal SMA generated by supplementary motor area signal extractor 140represents activity in the supplementary motor area ipsilateral to theintracranial stimulating electrode array (ISEA) 37. Supplementary motorarea signal extractor 140 amplifies signals which are unique to elementsof the EEG electrode array 51 which overlie the supplementary motorarea. The supplementary motor area receives neural signals via neuralprojections from the basal ganglia and exhibits decreased activity inpatients with Parkinson disease. The SMA is essential for sequentialmovements, which are often impaired in Parkinson's disease patients. TheSMA signal provides a quantitative measure of disease state and responseto therapy. The SMA signal is extracted from the anterior EEG leads,predominantly from those in the vicinity of the frontal cortex, andprovides a quantitative measure of disease state and response totherapy. Signals beta, alpha, theta, and delta consist of 13-30 Hz, 8-13Hz, 4-7 Hz, and 0.1-4 Hz activity, respectively.

Signal “resp” consists of 0.1-0.3 Hz activity and reflects respiration.Parkinson's disease patients exhibit irregular respiratory patternscharacterized by pauses and by abnormally deep breathing while at restand preceding speech. Assessment of respiratory irregularity as well asother parameters derived from such resp signal serve as quantitativemeasures of disease state and response to therapy.

Anterior EEG electrodes are also used to sense EMG signals, and the EMGsignals are processed to determine activity of muscles including but notlimited to those related to eye blinking activity. Processing of the EMGsignals is included in the FIG. 6 circuit block diagram which containsthe EEG signal processing component of signal processor 71. However, theprocessing could be incorporated into EMG signal processing component ofsignal processor 71 without departing from scope of the presentinvention. Conditioned EEG signal path 79 is additionally connected toinput of full wave rectifier 222, the output of which is connected tothe input of an envelope determiner 223. Envelope determiner 223includes an output connected to input of filter 224. Filter 224 ispreferably of the bandpass type with a passband range of 0.1 to 20 Hz.Filter 224 has an output connected to input of threshold discriminator225, the output of which is connected to EEG disease state estimator155.

Preferably, EMG signals arising from activity of at least one oforbicularis oculi (effecting eye closure), levator palpebrae (effectingeye opening), and other muscles the activity of which is associated witheyelid movement are sensed by anterior EEG electrodes. These EMG signalsare processed to determine eye blink events, and the rates andregularity of eye blinking activity are calculated. Frequency andirregularity of eyeblinking as well as blepharoclonus, or rhythmicfluttering of the eyelids, are quantified as measures of disease stateand response to therapy.

FIG. 7 is a block diagram of one embodiment of an accelerometer signalprocessor 235 which is incorporated into certain embodiments of signalprocessor 71. The accelerometer signal processor 235 processes signalsfrom accelerometer array 52. Conditioned accelerometer signal path 80 isconnected to an input of each of a plurality of filters 156, 160, 164,168, 172. The filters are preferably of the bandpass type with passbandsof 0.1-2 Hz, 2-3 Hz, 3-5 Hz, 7-8 Hz, and 8-13 Hz, respectively. Otherpassband frequency ranges may also be used. The output of each filter156, 160, 164, 168, 172 is connected to an associated full waverectifiers 157, 161, 165, 169, and 173, respectively. The output of eachrectifier 157, 161, 165, 169, and 173 is connected to an associatedenvelope determiners 158, 162, 166, 170, and 174, respectively. Outputsof envelope determiners 158, 162, 166, 170, and 174 are connected toinputs of an associated threshold discriminators 159, 163, 167, 171, and175, respectively.

Outputs of threshold discriminators 159, 163, 167, 171, 175 representepisodes of normal voluntary movement (Mv), low frequency intentiontremor (Til), resting tremor (Tr), high frequency intention tremor(Tih), and physiologic tremor (Tp), respectively. These outputs are eachconnected to an integrator 176 and a counter 177. Integrator 176generates outputs representative of the total activity of each of theabove types of movement over at least one period of time. As noted, sucha time period may be, for example, time since implementation, time sincelast visit to physician or health care provider, or some other timeinterval, weighted average of multiple time windows, or convolution ofselected activities with an arbitrary time window function.

Counter 177 generates outputs representative of the number of episodesof each of the above types of movements over at least one such period oftime. Outputs from integrator 176 and counter 177 are connect to anacceleration analyzer 178. Acceleration analyzer 178 calculatesproportions of tremor types, such as the rest and intention types,ratios of different types of tremor activity, the level of suppressionof resting tremor activity with voluntary movement, and assessment oftemporal patterns of movement and acceleration. Acceleration analyzer178 may perform some or all of these calculations, as well as othercalculations, on alternative embodiments of the present invention.Acceleration-based disease state estimator 179 receives input fromacceleration analyzer 178 and generates output representative of diseasestate based upon such input.

It should be understood that accelerometer signals may be sensed fromany individual or group of body components. For example, such signalsmay be sensed from joints, bones, and muscles. Furthermore, such signalsmay be processed in any well known manner, including the determinationof severity and frequency of occurrence of various tremor types. Thetypes of tremor have been described above with respect to FIG. 5.

FIG. 8 is a block diagram of one embodiment of an acoustic signalprocessor 236 which is included in certain embodiments of signalprocessor 71. Acoustic signal processor 236 processes signals fromacoustic transducer array 53. Conditioned acoustic signal path 81 isconnected to a full wave rectifier 180 and a spectral analyzer 185. Theoutput of full wave rectifier 180 is connected to an input of anenvelope determiner 181, an output of which is connected to an input ofa low threshold discriminator 182 and a high threshold discriminator183. Low threshold discriminator 182 and high threshold discriminator183 each have an output connected to an input of timer 184. Timer 184generates an output signal representing latency (Lat) and is connectedto acoustic analyzer 186. An output of acoustic analyzer 186 isconnected to an input of acoustic-based disease state estimator 187.Latency (Lat) represents the latency between initiation of vocalutterance and the subsequent achievement of a threshold level of vocalamplitude. Such a vocal amplitude level is set by high thresholddiscriminator 183 and may represent steady state vocal amplitude or apreset or dynamically varying threshold. Latency from voice onset toachievement of steady state volume may be delayed in patients withParkinson's disease and is calculated as a measure of disease state andresponse to therapy.

Acoustic analyzer 186 receives input from spectral analyzer 185. Therespiratory pattern is determined from rhythmic modulation of voice andbreathing sounds, sensed by elements of the acoustic transducer array53. Irregularity and pauses in respiration as well as abnormally deepbreathing patterns at rest and preceding speech are exhibited inParkinson's disease patients. Such parameters are quantified and used asestimates of disease state and response to therapy. Respirationdurations are quantified; abnormally deep respiration both during restand preceding speech are identified and used as indicators of diseasestate and response to therapy. Pauses in speech and decline in speechamplitude, or fading, are additionally monitored as indicators ofdisease state and response to therapy. Spectral composition of speech ismonitored and the change in spectral composition, reflective of changesof pharyngeal and laryngeal geometry, are quantified. Additionally, thefundamental vocal frequency; that is, the frequency at which theepiglottis vibrates, is extracted an that standard deviation of thefundamental vocal frequency is calculated over various time intervals asa quantified measure of the monotonic quality of speech characteristicof Parkinson's disease. This serves as yet another indicator of diseasestate and response to therapy.

FIG. 9 is block diagram of one embodiment of a peripheral nerveelectrode (PNE) signal processor 237 which is implemented in certainembodiments of signal processor 71. PNE signal processor 237 processessignals from peripheral nerve electrode array 54. These signals providedby peripheral nerve electrode array 54 are provided to PNE signalprocessor 237 via conditioned PNE signal path 82. Conditioned PNE signalpath 82 is connected to an input of a spike detector 188 and a filter191.

Spike detector 188 identifies action potentials. As noted, spikedetection techniques are well known to those skilled in the art, andgenerally employ low and high amplitude thresholds. Waveforms withamplitudes greater than the low threshold and lower than the highthreshold are determined to be action potentials. These thresholds maybe adjusted in real-time, and the low amplitude threshold is set abovethe amplitude of background noise and that of nearby cells not ofinterest, and the high amplitude threshold is set above the amplitude ofthe desired action potentials to allow their passage while eliminatinghigher amplitude noise spikes, such as artifacts arising from electricalstimulation currents. It should be understood that bandpass, notch, andother filtering techniques may also used to improve signal to noiseratio and the sensitivity and specific of spike detectors. Individualneuron action potentials are usually recorded using fine pointhigh-impedance electrodes, with impedances typically ranging from 1 to 5megohms. Alternatively, larger lower-impedance electrodes may be usedfor recording, in which case the signals obtained typically representaggregate activity of populations of neurons rather than actionpotentials from individual neurons. As noted above, peripheral nerveelectrode array 54 may include such electrodes as single unit recordingmicroelectrodes, multiple unit recording microelectrodes,intrafascicular electrodes, other intraneural electrodes, epineuralelectrodes, and any combination thereof.

A spike characterizer 189 determines firing patterns of individualneurons, including, for example, tonic activity, episodic activity andburst firing. Spike characterizer 189 receives the signals passed byspike detector 188 and calculates parameters that characterize thebehavior of the individual and groups of neurons, the activity of whichis sensed by peripheral nerve electrode array 54. Such characterizationincludes but is not limited to parameterization of spikes, bursts ofspikes, and overall neural activity patterns. Parameterization includesbut is not limited to calculation of frequencies of spikes, frequenciesof bursts of spikes, inter-spike intervals, spike amplitudes,peak-to-valley times, valley-to-peak times, spectral composition,positive phase amplitudes, negative phase amplitudes, andpositive-negative phase differential amplitudes. These parameters aredescribed in further detail below with reference to FIG. 14. Based onthis parameterization, spike characterizer 189 discriminates individualspikes and bursts originating from different neurons. The discriminationfacilitates aerial monitoring of activity of individual and groups ofneurons and the assessment and quantification of activity change,reflective of change in disease state and of response to therapy.

A spike analyzer 190 receives as input the parameters from spikecharacterizer 189, and extracts higher level information, including butnot limited to average spike frequencies, average frequencies o burstsof spikes, average interspike intervals, average spike amplitudes,standard deviations thereof, trends, and temporal patterning.

Preferably, spike analyzer 190 additionally calculates the rates ofchange of spike parameters. From prior and current rates of change,future behaviors may be predicted. Rates of change of the parametersinclude but are not limited to first, second, and third timederivatives. In alternative embodiments, spike analyzer 190 additionallycalculates weighted combinations of spike characteristics and performsconvolutions of spike waveforms with other spike waveforms, and otherpreset and varying waveforms. Such operations may be performed, forexample, for purposes including but not limited to autocorrelation anddigital filtering.

Spike analyzer 190 may receive additional input from accelerometers,such as those described above, including head mounted accelerometer 12,proximal accelerometer 28, enclosure mounted accelerometer 36, anddistal accelerometer 33. Spike analyzer 190 may receive indirect inputfrom these or other accelerometers, as well as from conditioned orprocessed signals arising therefrom. Such conditioned or processedsignals include, for example, the signal transmitted by conditionedaccelerometer signal path 80 (FIG. 7).

Spike analyzer 190 may receive additional input from EMG arrays. Asnoted, such EMG arrays may include, for example, proximal EMG electrodearray 45, enclosure-mounted EMG electrode array 46, and distal EMGelectrode array 47. Spike analyzer 190 may also receive indirect inputfrom these or other EMG electrode arrays, as well as from conditioned orprocessed signals arising therefrom. Such conditioned or processedsignals include but are not limited to the signal transmitted byconditioned EMG signal path 78 (FIG. 5). These additional inputs fromaccelerometers and EMG arrays facilitates the characterization ofneuronal firing patterns relative to activity of muscle groups andmovement of joints. Such characterization may include, for example,characterization of neuronal spike amplitudes and tuning of neuronalspike frequencies to movement, including but not limited to the signaltransmitted by conditioned EMG signal path 78.

The additional input from accelerometers and EMG arrays also facilitatesthe characterization of neuronal firing patterns relative to activity ofmuscle groups and movement of joints, including but not limited tocharacterization of neuronal spike amplitudes and tuning of neuronalspike frequencies to movement, including but not limited to movementvelocity and direction. These characterizations may be used to assessfunctioning of the sensorimotor system, including but not limited tomotor response time, and to measure the disease state and response totherapy.

Peripheral nerve electrode (PNE)-based single unit (SU) disease stateestimator 194 receives an input representative of the current neuronalactivity from spike characterizer 189. PNE-based single unit diseasestate estimator 194 may receive input representative of at least one ofseveral signals, including desired neuronal activity, actual neuronalactivity, and the difference between these quantities. The output fromestimator 194 may carry a single or a plurality of signals, consistentwith a representation of the disease state by a single or a multitude ofstate variables, respectively.

Filter 191 has an output connected to an input of spectral energycharacterizer 192. Spectral energy characterizer 192 calculates thespectral composition of the signals sensed by the peripheral nerveelectrode array 54. Spectral energy characterizer 192 provides outputsto each of spectral energy analyzer 193 and peripheral nerve electrode(PNE)-based multiple unit disease state estimator 232. Output ofspectral energy analyzer 193 is connected to an input of PNE-basedmultiple unit (MU) disease state estimator 232. PNE SU disease stateestimator 194 both receives input from and provides output to PNE MUdisease state estimator 232.

PNE MU disease state estimator 232 receives as an input signalsrepresentative of the current neuronal activity from spectral energycharacterizer 192. PNE MU disease state estimator 232 may receive inputrepresentative of at least one of several signals, including desiredneuronal activity, actual neuronal activity, and the difference betweenthese quantities. The output from PNE MU disease state estimator 232 maycarry a single or a plurality of signals, consistent with arepresentation of the disease state by a single or a multitude of statevariables, respectively.

It should be understood that inputs and outputs from each spike detector188, spike characterizer 189, spike analyzer 190, filter 191, spectralenergy characterizer 192, spectral energy analyzer 193, and PNE-basedsingle unit disease state estimator 194, and PNE-based multiple unitdisease state estimator 232 may each be comprised of individual signalsor a plurality of signals. It should also be understood that each ofthese the units, spike detector 188, spike characterizer 189, spikeanalyzer 190, filter 191, spectral energy characterizer 192, spectralenergy analyzer 193, and PNE-based single unit disease state estimator194, and PNE MU disease state estimator 232 may each have differentparameters and signal processing characteristics for each of themultiple signals processed. Modifications of this processing circuitrymay be made to accommodate various combinations of intraneuralelectrodes, used for single and multiple unit recordings, and epineuralelectrodes, used for compound action potential recordings, withoutdeparting from the present invention.

FIG. 11 is a schematic diagram of one embodiment of a patient-neuralmodulator system 999 illustrated in FIG. 2 with feedback control.Patient-neural modulator system 999 primarily includes an observer 228and a controller 229. An observer is a component of a control systemthat is known to those or ordinary skill in the art of control systems.An observer is a functional block in which variables, typicallyrepresented in software as parameter values or in hardware as electricalsignal amplitudes, represent states of the controlled system. Such acomponent is used in controlling systems in which one or more of thestate variables are not directly observable from the sensed signals. Anobserver essentially includes a simulated version of the controlledsystem. Its input are the same control law output signals delivered tothe controlled system, and its outputs are desired to match those sensedoutputs of the controlled system. The difference between the outputs ofthe observer and the measured outputs of the controlled system, that is,the outputs of a motor control portion of the patient's nervous systemin this case, are used to calculate an observer error signal which maythen be used to correct the observer error. Since the observer isimplemented in software or hardware, all of its signals, including allstate variables, are accessible. In a system such as the complex neuralcircuitry of the patient, one or more of the state variables may not be“observable”, that is directly measurable or calculatable based onmeasured values. In such a case, the state variables present in theobserver may be used as “estimates” of the actual state variables andincluded in the control law. The general use of “observers” forestimation of “unobservable” state variables is known to those skilledin the art of control theory. The use of observers for the estimation ofneural state variables, disease states, and responses to therapy is oneof the teachings of the present invention.

Observer 228 includes signal conditioning circuit 76 (FIG. 2) and signalprocessor 71 (FIGS. 2, 10). Signal processor 71, as noted, includesdisease state estimator module array (DSEMA) 229 and aggregate diseasestate estimator 195. Observer 228 receives patient output “y” frompatient 227. Patient output “y” is comprised of one or more signalsarising from patient 227. In one preferred embodiment patient output “y”includes one or more signals from EMG electrode array 50, EEG electrodearray 51, accelerometer array 52, acoustic transducer array 53,peripheral nerve electrode array 54, intracranial recording electrodearray 38, and intracranial stimulating electrode array 37. It should beunderstood that additional signals f the same or different type may alsobe included.

Control circuit 72 (FIG. 2) includes summator 226 which receives aninput from reference module 116, and a control law circuit block 231.Controller 229 includes the control law circuit lock 231 and outputstage circuit 77. Controller 229 generates a neural modulation waveforms“u”, described in detail below with reference to FIG. 13. The functionand operation of each of these modules is described in detail below.

Reference disease state “r”, generated by reference module 116, is anon-inverting input to summator 226, providing disease state and targetreference values for the single or plurality of control laws implementedin control law circuit block 231 introduced above with reference to FIG.2. Reference module 116 may also receive input from control circuit 72,facilitating the dynamic adjustment of reference values. Referencedisease state “r” may comprise a single or plurality of signals, each ofwhich may be zero, constant, or time-varying independent of the other.Disease state error “e” is output from summator 226 and input tocontroller 229. Disease state error “e”, which may comprise a single orplurality of signals, represents a difference between a desired diseasestate (represented by reference disease state “r”) and an actual diseasestate (represented by disease state estimate “x”). Other methods ofcalculating disease state estimate “x”, including but not limited tolinear or nonlinear combinations of reference disease state “r” anddisease state estimate “x”, may be employed without departing from thepresent invention. Controller 229 is comprised of control law circuitblock 231 and output stage circuit 77.

Disease state error “e” is input to control law circuit block 231 whichgenerates a control circuit output “uc.” Control law circuit block 231is connected to an input of output stage circuit 77. The output of thecontroller 229, which is generated by the output stage circuit 77, “u”,is delivered to patient 227 in the form of neural modulation waveforms,described in detail below with reference to FIG. 13.

Patient output “y” is input to signal conditioning circuit 76, theoutput of which is connected to the input of DSEMA 229. The output ofDSEMA 229 is provided to an aggregate disease state estimator 195, theoutput of which is the disease state estimate x. Disease state estimatex, which may be comprised of a single or plurality of signals, is aninverting input to summator 226.

Control law circuit block 231 receives disease state estimate x as anadditional input, for use in nonlinear, adaptive and other control laws.Reference module 116 receives input from DSEMA 229 and aggregate diseasestate estimator 195 for use in dynamically determining reference diseasestate r. Other modifications, including substitutions, additions, anddeletions, may be made to the control loop without departing from thepresent invention.

Control law circuit block 231 has an autocalibration mode in whichmultivariable sweeps through stimulation parameters and stimulatingelectrode configurations are performed to automate and expediteparameter and configuration optimization. This autocalibration featureenables rapid optimization of treatment, eliminating months ofiterations of trial and error in optimizing stimulation parameters andelectrode configuration necessitated by the prior technique of constantparameter stimulation. Additionally, this autocalibration featurepermits real-time adjustment and optimization of stimulation parametersand electrode configuration. This is particularly useful to overcomeincreases in electrode impedance which result from the body's normalresponse to implanted foreign bodies in which a fibrotic capsule iscommonly formed around the electrodes. Effects of shifts in electrodeposition relative to a target structures may be minimized by saidautocalibration feature. Detection of changes in electrode impedance andposition are facilitated by autocalibration feature. The autocalibrationfeature facilitates detection of changes in electrode impedance andposition. Notification of patient and health care provider allowsproactive action, including automated or manual adjustment of treatmentparameters and advance knowledge of impending electrode replacementneeds.

FIG. 12 is a schematic diagram of control circuit 72. As noted, controlcircuit 72 comprises control laws circuit block 231 and summator 226.Disease state error “e” is input to gain stages of control laws,including but not limited to at least one of proportional gain 197,differential gain 198, integral gain 199, nonlinear gain 200, adaptivegain 201, sliding gain 202, and model reference gain 203.

An output of each of these gain stages is connected to what is referredto herein as control law stages. In the illustrative embodiment, controllaw stages includes proportional controller 230, differential controller204, integral controller 205, nonlinear controller 206, adaptivecontroller 207, sliding controller 208, and model reference controller209, respectively.

Outputs of these control law stages are connected to weight stages,including proportional controller weight 210, differential controllerweight 211, integral controller weight 212, nonlinear controller weight213, adaptive controller weight 214, sliding controller weight 215, andmodel reference controller weight 216. Outputs of the weight stages arenoninverting inputs to summator 217, the output of which is controlcircuit output “uc”. The weight stages may be any combination of atleast one of constant, time varying, and nonlinear without departingfrom the present invention.

Disease state estimate x is input to nonlinear controller 206, adaptivecontroller 207, sliding controller 208, and model reference controller209. The control laws depicted are representative of one possibleimplementation; numerous variations, including substitutions, additions,and deletions, may be made without departing from the present invention.

The present invention optimizes the efficiency of energy used in thetreatment given to the patient by minimizing to a satisfactory level thestimulation intensity to provide the level of treatment magnitudenecessary to control disease symptoms without extending additionalenergy delivering unnecessary overtreatment. In the definition of thecontrol law, a command input or reference input (denoted as r in FIGS.11 and 12) specifies the target disease state. In the preferredembodiment, r specifies the target amplitude of tremor. The control lawgenerates an electrical stimulation magnitude just sufficient to reducethe patient's tremor to the target value. With this apparatus andmethod, the precise amount of electrical energy required is delivered,and overstimulation is avoided. In present stimulation systems, aconstant level of stimulation is delivered, resulting in either of twoundesirable scenarios when disease state and symptoms fluctuate: (1)undertreatment, i.e. tremor amplitude exceeds desirable level or (2)overtreatment or excess stimulation, in which more electrical energy isdelivered than is actually needed. In the overtreatment case, batterylife is unnecessarily reduced. The energy delivered to the tissue in theform of a stimulation signal represents a substantial portion of theenergy consumed by the implanted device; minimization of this energysubstantially extends battery life, with a consequent extension of timein between reoperations to replace expended batteries.

FIG. 13 is a schematic diagram of electrical stimulation waveforms forneural modulation. The illustrated ideal stimulus waveform is a chargebalanced biphasic current controlled electrical pulse train. Two cyclesof this waveform are depicted, each of which is made of a smallercathodic phase followed, after a short delay, by a larger anodic phase.In one preferred embodiment, a current controlled stimulus is delivered;and the “Stimulus Amplitude” represents stimulation current. A voltagecontrolled or other stimulus may be used without departing from thepresent invention. Similarly, other waveforms, including an anodic phasepreceding a cathodic phase, a monophasic pulse, a triphasic pulse, orthe waveform may be used without departing from the present invention.

The amplitude of the first phase, depicted here as cathodic, is given bypulse amplitude 1 PA1; the amplitude of the second phase, depicted hereas anodic, is given by pulse amplitude 2 PA2. The durations of the firstand second phases are pulse width 1 PW1 and pulse width 1 PW2,respectively. Phase 1 and phase 2 are separated by a brief delay d.Waveforms repeat with a stimulation period T, defining the stimulationfrequency as f=1/T.

The area under the curve for each phase represents the charge Qtransferred, and in the preferred embodiment, these quantities are equaland opposite for the cathodic (Q1) and anodic (Q2) pulses, i.e. Q=Q1=Q2.For rectangular pulses, the charge transferred per pulse is given byQ1=PA1*PW1 and Q2=PA2*PW2. The charge balancing constraint given by−Q1=Q2 imposes the relation PA1*PW1=−PA2*PW2. Departure from the chargebalancing constraint, as is desired for optimal function of certainelectrode materials, in included in the present invention.

The stimulus amplitudes PA1 and PA2, durations PW1 and PW2, frequency f,or a combination thereof may be varied to modulate the intensity of thesaid stimulus. A series of stimulus waveforms may be delivered as aburst, in which case the number of stimuli per burst, the frequency ofwaveforms within the said burst, the frequency at which the bursts arerepeated, or a combination thereof may additionally be varied tomodulate the stimulus intensity.

Typical values for stimulation parameters include f=100-300 Hz, PA1 andPA2 range from 10 microamps to 10 milliamps, PW1 and PW2 range from 50microseconds to 100 milliseconds. These values are representative, anddeparture from these ranges is included in the apparatus and method ofthe present invention.

FIG. 14 is a schematic diagram of one example of the recorded waveforms.This represents an individual action potential from a single cellrecording, typically recorded from intracranial microelectrodes.Aggregates of multiple such waveforms are recorded from largerintracranial electrodes. The action potentials may be characterizedaccording t a set of parameters including but not limited to time tovalley 1 TV1, time to peak 1 TP1, time to valley 2 TV2, amplitude ofvalley 1 AV1, amplitude of peak 1 AP1, amplitude of valley 2 AV2, andalgebraic combinations and polarity reversals thereof.

When recording activity from more than one cell, said characterizationfacilitates discrimination of waveforms by individual recorded cell. Thediscrimination allows activity of a plurality of cells to beindividually followed over time. The parameterization may be performedseparately on signals recorded from different electrodes. Alternatively,said parameterization may be performed on signals pooled from multipleelectrodes.

Following is a description of a general form for representing diseasestate.

Disease State DS is a vector of individual disease states, includingintrinsic disease states DSI and extrinsic disease states DSE:DS=[DS₁ DS_(E)]

Intrinsic disease states and extrinsic disease states are, themselvesvectors of individual disease states:DS₁=[DS₁₁ DS₁₂ DS₁₃ . . . DS_(IN)]DS_(E)=[DS_(E1) DS_(E2) DS_(E3) . . . DS_(EM)]

Intrinsic Disease States include those disease states which characterizethe state of disease at a given point in time. Extrinsic Disease Statesinclude variations of intrinsic disease states, including but notlimited to cyclical variations in Intrinsic Disease States, variationsin Intrinsic Disease States which occur in response to external events,and variations in Intrinsic Disease States which occur in response tolevels of and changes in levels of electrical stimulation. Said externalevents include but are not limited to pharmacologic dosing, consumptionof meals, awakening, falling asleep, transitioning from Parkinsonian“on” state to Parkinsonian “off” state, transitioning from Parkinsonian“off” state to Parkinsonian “on” state.

Each of Intrinsic Disease States and Extrinsic Disease States includebut are not limited to those defined herein; additional disease statesand definitions thereof may be added without departing from the presentinvention.

The first intrinsic disease state DS₁₁ represents the level of restingtremorDS₁₁=RT_(N)

Where Normalized Resting Tremor Magnitude RT_(N) is given by:RT _(N) =T _(A,3-5) *W _(TA,3-5) +T _(E,3-5) *W _(TE,3-5) +T _(P,3-5) *W_(PE,3-5) +T _(C,3-5) +W _(TC,3-5) +T _(N,3-5) *W _(TN,3-5) +T _(S,3-5)*W _(TS,3-5) +T _(E,3-5) *W _(TE,3-5)

Where the factors from which the Resting Tremor Magnitude RT_(N) isdetermined, representing estimates of the magnitude of 3-5 Hertzmovement of selected body segments, including but not limited to limbs,torso, and head are:

T_(A,3-5)=Tremor level determined by acceleration monitoring

W_(TA,3-5)=Weighting factor for tremor TA,3-5

T_(E,3-5)=Tremor level determined by electromyographic (EMG) monitoring

W_(TE,3-5)=Weighting factor for tremor TE,3-5

T_(P,3-5)=Tremor level determined by peripheral nerve electrodemonitoring

W_(TP,3-5)=Weighting factor for tremor T_(P,3-5)

T_(C,3-5)=Tremor level determined by cortical electrode monitoring

W_(TC,3-5)=Weighting factor for tremor T_(C,3-5)

T_(N,3-5)=Tremor level determined by neural monitoring, includingsubcortical nuclei, white matter tracts, and spinal cord neurons

W_(TN,3-5)=Weighting factor for tremor T_(N,3-5)

T_(S,3-5)=Tremor level determined by acoustic sensor monitoring

W_(TS,3-5)=Weighting factor for tremor T_(S,3-5)

Weighting factors are adjusted after implantation to achievenormalization of RT_(N) and to allow for selective weighting of tremorlevels as determined from signals arising from various sensors,including but not limited to those listed.

These calculations may be implemented in analog hardware, digitalhardware, software, or other form. In the preferred embodiment, valuesare implemented as 16-bit variables; ranges for said weighting factorsand tremor levels are 0 to 65535. These ranges may be changed orimplemented in analog form without departing from the present invention.

The second intrinsic disease state DS₁₂ represents the level ofdyskinesia:DS₁₂=D_(N)

Where Normalized Dyskinesia Magnitude D_(N) is given by:D _(N) =D _(A) *W _(DA) +T _(E) *W _(TE) +T _(P) *W _(PE) +T _(C) +W_(TC) +T _(N) *W _(TN) +T _(S) *W _(TS) +T _(E) *W _(TE)

Where

D_(A,3-5)=Dyskinesia level determined by acceleration monitoring

W_(DA,3-5)=Weighting factor for Dyskinesia D_(A,3-5)

D_(E,3-5)=Dyskinesia level determined by electromyographic (EMG)monitoring

W_(DE,3-5)=Weighting factor for Dyskinesia D_(E,3-5)

D_(P,3-5)=Dyskinesia level determined by peripheral nerve electrodemonitoring

W_(DP,3-5)=Weighting factor for Dyskinesia D_(P,3-5)

D_(C,3-5)=Dyskinesia level determined by cortical electrode monitoring

W_(DC,3-5)=Weighting factor for Dyskinesia D_(C,3-5)

D_(N,3-5)=Dyskinesia level determined by neural monitoring, includingsubcortical nuclei, white matter tracts, and spinal cord neurons

W_(DN,3-5)=Weighting factor for Dyskinesia D_(N,3-5)

D_(S,3-5)=Dyskinesia level determined by acoustic sensor monitoring

W_(DS,3-5)=Weighting factor for Dyskinesia D_(S,3-5)

The third intrinsic disease state DS₁₃ represents the level of rigidity.DS₁₃=R_(N)

Where Normalized Rigidity Magnitude R_(N) is given by:R _(N) =R _(A) *W _(RA) +R _(E) *W _(RE) +R _(P) *W _(RE) +R _(C) +W_(RC) +R _(N) *W _(RN) +R _(S) *W _(RS) +R _(E) *W _(RE)

Where

R_(A,3-5)=Rigidity level determined by acceleration monitoring

W_(RA,3-5)=Weighting factor for Rigidity R_(A,3-5)

R_(E,3-5)=Rigidity level determined by electromyographic (EMG)monitoring

W_(RE,3-5)=Weighting factor for Rigidity RE,3-5

R_(P,3-5)=Rigidity level determined by peripheral nerve electrodemonitoring

W_(RP,3-5)=Weighting factor for Rigidity R_(P,3-5)

R_(C,3-5)=Rigidity level determined by cortical electrode monitoring

W_(RC,3-5)=Weighting factor for Rigidity R_(C,3-5)

R_(N,3-5)=Rigidity level determined by neural monitoring, includingsubcortical nuclei, white matter tracts, and spinal cord neurons

W_(RN,3-5)=Weighting factor for Rigidity R_(N,3-5)

R_(S,3-5)=Rigidity level determined by acoustic sensor monitoring

W_(RS,3-5)=Weighting factor for Rigidity R_(S,3-5)

The fourth intrinsic disease state DS₁₄ represents the level ofbradykinesia.DS₁₄=B_(N)

Where Normalized Bradykinesia Magnitude B_(N) is given by:B_(N) =B _(A) *W _(BA) +B _(E) *W _(BE) +B _(P) *W _(PE) +B _(C) +W_(BC) +B _(N) *W _(BN) +B _(S) *W _(BS) +B _(E) *W _(BE)

Where

R_(A)=Bradykinesia level determined by acceleration monitoring

W_(RA)=Weighting factor for Bradykinesia R_(A)

R_(E)=Bradykinesia level determined by electromyographic (EMG)monitoring

W_(RE)=Weighting factor for Bradykinesia RE

R_(P)=Bradykinesia level determined by peripheral nerve electrodemonitoring

W_(RP)=Weighting factor for Bradykinesia R_(P)

R_(C)=Bradykinesia level determined by cortical electrode monitoring.

W_(RC)=Weighting factor for Bradykinesia R_(C)

R_(N)=Bradykinesia level determined by neural monitoring, includingsubcortical nuclei, white matter tracts, and spinal cord neurons

W_(RN)=Weighting factor for Bradykinesia R_(N)

R_(S)=Bradykinesia level determined by acoustic sensor monitoring

W_(RS)=Weighting factor for Bradykinesia R_(S)

The control law drives these disease states toward their referencevalues, nominally 0, according to a vector of weights, establishing aprioritization.

Side effects and other parameters, such as power consumption and currentmagnitude, are also quantified and minimized according to a costfunction.

One advantage of the present invention is that it provides prediction offuture symptomatology, cognitive and neuromotor functionality, andtreatment magnitude requirements. Such predictions may be based onpreset, learned and real-time sensed parameters as well as input fromthe patient, physician or other person or system. The prediction offuture symptomatology is based upon any of several weighted combinationof parameters. Based upon prior characterization of the circadianfluctuation in symptomatology (that is, tremor magnitude for deep brainstimulation or level of depression for stimulation of other sitesincluding locus ceruleus), future fluctuations may be predicted. Anestimate, or model, of fluctuation may be based upon a combination ofpreset, learned, and real-time sensed parameters. Preset parameters arederived from clinical studies designed specifically for the purpose ofgathering such data, or from estimates extracted from data gleaned frompublished literature. Real-time sensed parameters are derived from thecurrent states (and changes, i.e. derivatives and other processedsignals, thereof) of sensed and processed signals. Learned parametersare based upon the time histories of previously sensed signals. Forexample, the circadian fluctuation in tremor amplitude may be sensed; aweighted average of this data collected over numerous prior daysprovides as estimate of the expected tremor amplitude as well as astandard deviation and other statistical parameters to characterize theanticipated tremor amplitude. Similarly, in the presence of closed-loopfeedback, the level of stimulation required to reduce or eliminatetremor may be used as an estimate of the “amplitude” or state of theunderlying disease.

Another advantage of the present invention is that it performs automateddetermination of the optimum magnitude of treatment—by sensing andquantifying the magnitude and frequency of tremor activity in thepatient, a quantitative representation of the level or “state” of thedisease is determined. The disease state is monitored as treatmentparameters are automatically varied, and the local or absolute minimumin disease state is achieved as the optimal set of stimulationparameters is converged upon. The disease state may be represented as asingle value or a vector or matrix of values; in the latter two cases, amultivariable optimization algorithm is employed with appropriateweighting factors.

Having now described several embodiments of the invention, it should beapparent to those skilled in the art that the foregoing is merelyillustrative and not limiting, having been presented by way of exampleonly. For example, all signal paths may transmit a single or pluralityof signals without departing from the present invention. Numerousmodifications and other embodiments are within the scope of one ofordinary skill in the art and are contemplated as falling within thescope of the invention as defined by the appended claims.

1. A system for providing neurological disease state information to apatient, the system comprising: one or more sensors that sense at leastone signal that comprise feature(s) that are indicative of aneurological disease state; a signal processing assembly incommunication with the one or more sensors that processes the at leastone signal to estimate the neurological disease state; and a patientinterface module in communication with the signal processing assemblythat communicates with a patient an output that is indicative of theestimated neurological disease state.
 2. The system of claim 1 whereinthe output from the patient interface module facilitates monitoring of atime history of the estimated disease state.
 3. The system of claim 1wherein the output from the patient interface module facilitatesmonitoring of an estimated future disease state.
 4. The system of claim1 comprising a treatment assembly in communication with the signalprocessing assembly, wherein the signal processing assembly isconfigured to deliver a therapy to the patient through the treatmentassembly that is based at least in part on the estimated neurologicaldisease state.
 5. The system of claim 4 wherein the treatment assemblycomprises at least one electrode that is configured to deliverneurostimulation to a nervous system component.
 6. The system of claim 5wherein the at least one electrode is configured to be coupled to aperipheral nerve.
 7. The system of claim 6 wherein the peripheral nervecomprises a vagus nerve.
 8. The system of claim 5 wherein the electrodeis configured to be placed in an intracranial location.
 9. The system ofclaim 4 wherein the treatment assembly comprises a medication dispenser.10. The system of claim 4 wherein the signal processing assembly isconfigured to compare the estimated neurological disease state with areference neurological disease state, wherein the comparison of theestimated neurological disease state with the reference neurologicaldisease state is used at least in part to generate parameters of thetherapy.
 11. The system of claim 1 wherein the signal processingassembly comprises a module for calculating a control law.
 12. Thesystem of claim 1 wherein at least one of the signal processing assemblyand patient interface module comprises a memory for storing theestimated neurological disease state.
 13. A method of estimating apatient's neurological disease state, the method comprising: sensing atleast one neurological signal from the patient; processing the at leastone signal to estimate the patient's neurological disease state; andcommunicating to the patient a patient output that is indicative of theestimated neurological disease state.
 14. The method of claim 13 whereincommunicating facilitates monitoring of a time history of the estimatedneurological disease state.
 15. The method of claim 13 whereincommunicating facilitates monitoring of an estimated future neurologicaldisease state.
 16. The method of claim 13 wherein processing andcommunicating to the patient are performed by an implanted device. 17.The method of claim 13 wherein processing is carried out in an implantedsignal processing assembly and communicating to the patient is carriedout with an external patient interface module, the method furthercomprising percutaneously transmitting the processed signal(s) from thesignal processing assembly to the patient interface module.
 18. Themethod of claim 13 further comprising communicating an output to asupervisory module.
 19. The method of claim 13 wherein the patientoutput is provided to the patient in real-time.
 20. The method of claim13 wherein the patient output is indicative of a patient's response totherapy.
 21. The method of claim 13 comprising delivering a therapy tothe patient that is based at least in part on the estimated neurologicaldisease state.
 22. The method of claim 21 wherein the therapy comprisesdelivery of a neuromodulation signal.
 23. The method of claim 22 whereinthe therapy comprises delivery of a medication.
 24. The method of claim22 wherein the therapy comprises delivery of an electrical signal. 25.The method of claim 22 wherein the neuromodulation signal is deliveredto a nervous system component.
 26. The method of claim 22 wherein theneuromodulation signal is delivered to one or more peripheral nerves.27. The method of claim 26 wherein the peripheral nerve comprises thevagus nerve.
 28. The method of claim 13 wherein sensing is carried outby coupling one or more sensors to a nervous system component.
 29. Themethod of claim 13 comprising storing the estimated neurological diseasestate in a memory.
 30. A system for providing epilepsy disease stateinformation to a patient, the system comprising: one or more sensorscoupled to a nervous system component that sense at least one signalthat comprise characteristic(s) that are indicative of an epilepsydisease state; an implanted digital signal processing assembly incommunication with the one or more sensors that generates an estimatedepilepsy disease state based at least on part on the at least one signalfrom the one or more sensors; and a patient interface module that isexternal to the patient's body in wireless communication with theimplanted signal processing assembly, wherein the patient interfacemodule communicates to the patient an output that allows the patient tomonitor their epilepsy disease state.
 31. The system of claim 30 whereinthe output is performed substantially continuously.
 32. A system forproviding neurological disease state information to a patient, thesystem comprising: one or more sensors that sense at least one signalthat comprise characteristic(s) that are indicative of a neurologicaldisease state; a signal processing assembly in communication with theone or more sensors that processes the at least one signal to estimatethe neurological disease state; and a patient interface module incommunication with the signal processing assembly that is configured toallow the patient to query the signal processing assembly to communicatethe estimated neurological disease state to the patient.
 33. A method ofcommunicating a patient's neurological disease state to the patient, themethod comprising: sensing at least one neurological signal from thepatient; processing the at least one signal with a signal processor toestimate the patient's neurological disease state; querying the signalprocessor with a patient interface module; and communicating to thepatient interface module an output that is indicative of the estimatedneurological disease state.